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View file @ f42429f6
unique_id,ds,NHITS
FOODS_1,2016-04-25,2504.762
FOODS_1,2016-04-26,2467.719
FOODS_1,2016-04-27,2430.538
FOODS_1,2016-04-28,2545.7861
FOODS_1,2016-04-29,3158.0547
FOODS_1,2016-04-30,3675.847
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FOODS_1,2016-05-02,2603.3481
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FOODS_1,2016-05-12,2711.9429
FOODS_1,2016-05-13,3265.7722
FOODS_1,2016-05-14,3737.7068
FOODS_1,2016-05-15,3250.004
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FOODS_1,2016-05-17,2565.505
FOODS_1,2016-05-18,2516.273
FOODS_1,2016-05-19,2611.7502
FOODS_1,2016-05-20,3218.6646
FOODS_1,2016-05-21,3715.1863
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FOODS_2,2016-04-26,4537.3613
FOODS_2,2016-04-27,4325.717
FOODS_2,2016-04-28,4522.6455
FOODS_2,2016-04-29,5086.342
FOODS_2,2016-04-30,6492.3867
FOODS_2,2016-05-01,7200.9097
FOODS_2,2016-05-02,6160.0244
FOODS_2,2016-05-03,5388.8823
FOODS_2,2016-05-04,5440.1694
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FOODS_2,2016-05-06,6055.701
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FOODS_2,2016-05-09,6759.285
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FOODS_2,2016-05-19,4596.719
FOODS_2,2016-05-20,4982.272
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FOODS_2,2016-05-22,6697.241
FOODS_3,2016-04-25,17230.807
FOODS_3,2016-04-26,15789.233
FOODS_3,2016-04-27,15369.597
FOODS_3,2016-04-28,15617.65
FOODS_3,2016-04-29,18008.902
FOODS_3,2016-04-30,23040.523
FOODS_3,2016-05-01,23571.592
FOODS_3,2016-05-02,18340.871
FOODS_3,2016-05-03,16562.752
FOODS_3,2016-05-04,16747.717
FOODS_3,2016-05-05,17052.62
FOODS_3,2016-05-06,19778.832
FOODS_3,2016-05-07,24705.719
FOODS_3,2016-05-08,24711.145
FOODS_3,2016-05-09,19328.555
FOODS_3,2016-05-10,17409.87
FOODS_3,2016-05-11,17146.104
FOODS_3,2016-05-12,16995.588
FOODS_3,2016-05-13,19478.918
FOODS_3,2016-05-14,24267.672
FOODS_3,2016-05-15,24103.676
FOODS_3,2016-05-16,18767.125
FOODS_3,2016-05-17,16687.102
FOODS_3,2016-05-18,16438.53
FOODS_3,2016-05-19,15937.973
FOODS_3,2016-05-20,18391.8
FOODS_3,2016-05-21,23098.781
FOODS_3,2016-05-22,23017.9
HOBBIES_1,2016-04-25,3293.069
HOBBIES_1,2016-04-26,3175.8167
HOBBIES_1,2016-04-27,3104.7383
HOBBIES_1,2016-04-28,3130.194
HOBBIES_1,2016-04-29,3652.3372
HOBBIES_1,2016-04-30,4440.7046
HOBBIES_1,2016-05-01,4260.2666
HOBBIES_1,2016-05-02,3375.5198
HOBBIES_1,2016-05-03,3186.3364
HOBBIES_1,2016-05-04,3148.969
HOBBIES_1,2016-05-05,3190.6125
HOBBIES_1,2016-05-06,3787.1
HOBBIES_1,2016-05-07,4503.3115
HOBBIES_1,2016-05-08,4216.017
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HOBBIES_1,2016-05-10,3130.4082
HOBBIES_1,2016-05-11,3052.7866
HOBBIES_1,2016-05-12,3083.465
HOBBIES_1,2016-05-13,3624.693
HOBBIES_1,2016-05-14,4420.6533
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HOBBIES_1,2016-05-16,3327.1152
HOBBIES_1,2016-05-17,3115.9226
HOBBIES_1,2016-05-18,3068.1096
HOBBIES_1,2016-05-19,3066.814
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HOBBIES_1,2016-05-22,4195.486
HOBBIES_2,2016-04-25,359.69662
HOBBIES_2,2016-04-26,337.56714
HOBBIES_2,2016-04-27,336.36066
HOBBIES_2,2016-04-28,346.82523
HOBBIES_2,2016-04-29,385.13193
HOBBIES_2,2016-04-30,448.0414
HOBBIES_2,2016-05-01,428.58112
HOBBIES_2,2016-05-02,347.9329
HOBBIES_2,2016-05-03,325.00256
HOBBIES_2,2016-05-04,332.32742
HOBBIES_2,2016-05-05,348.48566
HOBBIES_2,2016-05-06,397.46094
HOBBIES_2,2016-05-07,463.35196
HOBBIES_2,2016-05-08,445.56622
HOBBIES_2,2016-05-09,360.51288
HOBBIES_2,2016-05-10,336.52014
HOBBIES_2,2016-05-11,344.28247
HOBBIES_2,2016-05-12,358.7079
HOBBIES_2,2016-05-13,409.6697
HOBBIES_2,2016-05-14,475.46484
HOBBIES_2,2016-05-15,455.52716
HOBBIES_2,2016-05-16,381.3134
HOBBIES_2,2016-05-17,355.26096
HOBBIES_2,2016-05-18,351.80453
HOBBIES_2,2016-05-19,363.197
HOBBIES_2,2016-05-20,404.1736
HOBBIES_2,2016-05-21,471.72308
HOBBIES_2,2016-05-22,452.07477
HOUSEHOLD_1,2016-04-25,7322.5625
HOUSEHOLD_1,2016-04-26,6919.1865
HOUSEHOLD_1,2016-04-27,6807.973
HOUSEHOLD_1,2016-04-28,6930.8086
HOUSEHOLD_1,2016-04-29,8116.5
HOUSEHOLD_1,2016-04-30,10300.233
HOUSEHOLD_1,2016-05-01,10267.285
HOUSEHOLD_1,2016-05-02,7921.134
HOUSEHOLD_1,2016-05-03,7284.6235
HOUSEHOLD_1,2016-05-04,7280.303
HOUSEHOLD_1,2016-05-05,7267.668
HOUSEHOLD_1,2016-05-06,8540.003
HOUSEHOLD_1,2016-05-07,10456.705
HOUSEHOLD_1,2016-05-08,10088.888
HOUSEHOLD_1,2016-05-09,7604.306
HOUSEHOLD_1,2016-05-10,6985.8867
HOUSEHOLD_1,2016-05-11,6745.779
HOUSEHOLD_1,2016-05-12,6693.4326
HOUSEHOLD_1,2016-05-13,7825.8516
HOUSEHOLD_1,2016-05-14,9895.505
HOUSEHOLD_1,2016-05-15,9434.72
HOUSEHOLD_1,2016-05-16,7175.374
HOUSEHOLD_1,2016-05-17,6486.9434
HOUSEHOLD_1,2016-05-18,6495.8496
HOUSEHOLD_1,2016-05-19,6345.693
HOUSEHOLD_1,2016-05-20,7672.4624
HOUSEHOLD_1,2016-05-21,9825.944
HOUSEHOLD_1,2016-05-22,9556.082
HOUSEHOLD_2,2016-04-25,1931.113
HOUSEHOLD_2,2016-04-26,1782.4025
HOUSEHOLD_2,2016-04-27,1758.2009
HOUSEHOLD_2,2016-04-28,1763.3115
HOUSEHOLD_2,2016-04-29,2031.7482
HOUSEHOLD_2,2016-04-30,2627.6375
HOUSEHOLD_2,2016-05-01,2618.0635
HOUSEHOLD_2,2016-05-02,1939.3312
HOUSEHOLD_2,2016-05-03,1775.9213
HOUSEHOLD_2,2016-05-04,1784.6147
HOUSEHOLD_2,2016-05-05,1768.6473
HOUSEHOLD_2,2016-05-06,2086.8005
HOUSEHOLD_2,2016-05-07,2645.3496
HOUSEHOLD_2,2016-05-08,2584.8975
HOUSEHOLD_2,2016-05-09,1910.047
HOUSEHOLD_2,2016-05-10,1752.4678
HOUSEHOLD_2,2016-05-11,1736.5
HOUSEHOLD_2,2016-05-12,1733.4746
HOUSEHOLD_2,2016-05-13,2037.8102
HOUSEHOLD_2,2016-05-14,2586.608
HOUSEHOLD_2,2016-05-15,2516.9414
HOUSEHOLD_2,2016-05-16,1902.0074
HOUSEHOLD_2,2016-05-17,1732.1702
HOUSEHOLD_2,2016-05-18,1742.648
HOUSEHOLD_2,2016-05-19,1724.6858
HOUSEHOLD_2,2016-05-20,2035.2493
HOUSEHOLD_2,2016-05-21,2608.6196
HOUSEHOLD_2,2016-05-22,2549.3857
nbs/docs/capabilities/000_capabilities.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "markdown",
"id": "6de758ee-a0d2-4b3f-acff-eed419dd17c5",
"metadata": {},
"source": [
"# Capabilities"
]
},
{
"cell_type": "markdown",
"id": "5d267032-535b-4b7b-b7d3-d2db8f673af6",
"metadata": {},
"source": [
"This section offers an overview of capabilities of TimeGPT"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
nbs/docs/capabilities/forecast/00_forecast.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "markdown",
"id": "6de758ee-a0d2-4b3f-acff-eed419dd17c5",
"metadata": {},
"source": [
"# Forecast"
]
},
{
"cell_type": "markdown",
"id": "5d267032-535b-4b7b-b7d3-d2db8f673af6",
"metadata": {},
"source": [
"This section shows the capabilities TimeGPT offers for forecasting.\n",
"\n",
"TimeGPT is capable of zero-shot forecasting a wide variety of time series from different domains, thanks to its pretraining on a vast amount of time series data.\n",
"\n",
"Here, you will find recipes for the following tasks:\n",
"\n",
"* [Zero-shot forecasting](https://docs.nixtla.io/docs/capabilities-forecast-quickstart)\n",
"\n",
"* [Forecasting with exogenous variables](https://docs.nixtla.io/docs/capabilities-forecast-add_exogenous_variables)\n",
"\n",
"* [Forecasting with holidays and special dates](https://docs.nixtla.io/docs/capabilities-forecast-add_holidays_and_special_dates)\n",
"\n",
"* [Forecasting with categorical variables](https://docs.nixtla.io/docs/capabilities-forecast-add_categorical_variables)\n",
"\n",
"* [Long-horizon forecasting](https://docs.nixtla.io/docs/capabilities-forecast-long_horizon_forecasting)\n",
"\n",
"* [Forecasting multiple series](https://docs.nixtla.io/docs/capabilities-forecast-multiple_series_forecasting)\n",
"\n",
"* [Fine-tuning TimeGPT](https://docs.nixtla.io/docs/capabilities-forecast-fine_tuning)\n",
"\n",
"* [Fine-tuning with a specific loss function](https://docs.nixtla.io/docs/capabilities-forecast-finetuning_with_a_custom_loss_function)\n",
"\n",
"* [Cross-validation](https://docs.nixtla.io/docs/capabilities-forecast-cross_validation)\n",
"\n",
"* [Adding prediction intervals](https://docs.nixtla.io/docs/capabilities-forecast-predictions_intervals)\n",
"\n",
"* [Dealing with irregular timestamps](https://docs.nixtla.io/docs/capabilities-forecast-irregular_timestamps)"
]
},
{
"cell_type": "markdown",
"id": "ec7b0357",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
nbs/docs/capabilities/forecast/01_quickstart.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quickstart\n",
"\n",
"To forecast with TimeGPT, call the `forecast` method. Pass your DataFrame and specify your target and time column names. Then plot the predictions using the `plot` method. You can read about data requierments [here](https://docs.nixtla.io/docs/getting-started-data_requirements). \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/01_quickstart.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/01_quickstart')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, set the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"INFO:nixtla.nixtla_client:Restricting input...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 2400x350 with 1 Axes>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df,\n",
" h=12,\n",
" time_col='timestamp',\n",
" target_col=\"value\"\n",
")\n",
"\n",
"# Plot predictions\n",
"nixtla_client.plot(\n",
" df=df, \n",
" forecasts_df=forecast_df, \n",
" time_col='timestamp', \n",
" target_col='value'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you use an Azure AI endpoint, set `model=\"azureai\"`\n",
">\n",
"> `nixtla_client.detect_anomalies(..., model=\"azureai\")`\n",
">\n",
"> For the public API, two models are supported: `timegpt-1` and `timegpt-1-long-horizon`.\n",
"> \n",
"> By default, `timegpt-1` is used. See [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) for details on using `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/02_exogenous_variables.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add exogenous variables\n",
"\n",
"To model with exogenous features, you have two options:\n",
"1. Use historical exogenous variables: include these variables in the DataFrame you pass to the `forecast` method\n",
"2. Use future exogenous variables: include these variables in the DataFrame you pass to the `forecast` method and provide the future values of these exogenous features over the forecast horizon using the `X_df` parameter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/02_exogenous_variables')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, set the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Historical exogenous variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read data\n",
"df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df, \n",
" h=24,\n",
" id_col='unique_id',\n",
" target_col='y',\n",
" time_col='ds',\n",
" # Add the columns of `df` that will be considered as historical\n",
" hist_exog_list=['Exogenous1', 'Exogenous2', 'day_0', 'day_1', 'day_2', 'day_3', 'day_4', 'day_5', 'day_6']\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Future exogenous variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read data\n",
"import numpy as np\n",
"df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')\n",
"\n",
"# Load the future value of exogenous variables over the forecast horizon\n",
"future_ex_vars_df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-future-ex-vars.csv')\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df, \n",
" X_df=future_ex_vars_df, \n",
" h=24,\n",
" id_col='unique_id',\n",
" target_col='y',\n",
" time_col='ds'\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Historical and future exogenous variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read data\n",
"df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')\n",
"\n",
"# Load the future value of exogenous variables over the forecast horizon\n",
"future_ex_vars_df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-future-ex-vars.csv')\n",
"\n",
"# We will only use 2 exogenous of future_ex_vars_df\n",
"future_ex_vars_df = future_ex_vars_df[[\"unique_id\", \"ds\", \"Exogenous1\", \"Exogenous2\"]]\n",
"# To pass historical exogenous variables, we need to add the list of columns\n",
"# in the `hist_exog_list` as follows.\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df, \n",
" X_df=future_ex_vars_df, \n",
" h=24,\n",
" id_col='unique_id',\n",
" target_col='y',\n",
" time_col='ds',\n",
" # Add the columns of `df` that will be considered as historical\n",
" hist_exog_list=['day_0', 'day_1', 'day_2', 'day_3', 'day_4', 'day_5', 'day_6']\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you use an Azure AI endpoint, set `model=\"azureai\"`\n",
">\n",
"> `nixtla_client.detect_anomalies(..., model=\"azureai\")`\n",
">\n",
"> For the public API, two models are supported: `timegpt-1` and `timegpt-1-long-horizon`.\n",
"> \n",
"> By default, `timegpt-1` is used. See [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) for details on using `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details on using exogenous features with TimeGPT, read our in-depth tutorials on [Exogenous variables](https://docs.nixtla.io/docs/tutorials-exogenous_variables) and on [Categorical variables](https://docs.nixtla.io/docs/tutorials-categorical_variables)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
nbs/docs/capabilities/forecast/03_holidays_special_dates.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add holidays and special dates\n",
"\n",
"You can create DataFrames specifying holidays for particular countries and specify your own special dates to include them as features for forecasting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/03_holidays_special_dates.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/03_holidays_special_dates')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient\n",
"from nixtla.date_features import CountryHolidays\n",
"from nixtla.date_features import SpecialDates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get country holidays for the US\n",
"c_holidays = CountryHolidays(countries=['US'])\n",
"periods = 365 * 1\n",
"dates = pd.date_range(end='2023-09-01', periods=periods)\n",
"holidays_df = c_holidays(dates)\n",
"\n",
"# Specify your own special dates\n",
"special_dates = SpecialDates(\n",
" special_dates={\n",
" 'Important Dates': ['2021-02-26', '2020-02-26'],\n",
" 'Very Important Dates': ['2021-01-26', '2020-01-26', '2019-01-26']\n",
" }\n",
")\n",
"periods = 365 * 1\n",
"dates = pd.date_range(end='2023-09-01', periods=periods)\n",
"special_dates_df = special_dates(dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a detailed guide on using special dates and holidays, read our tutorial on [Holidays and special dates](https://docs.nixtla.io/docs/tutorials-holidays_and_special_dates)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/04_categorical_variables.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add categorical variables\n",
"\n",
"TimeGPT supports categorical variables and we can create them using `SpecialDates`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/04_categorical_variables.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/04_categorical_variables')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import datetime\n",
"from nixtla import NixtlaClient\n",
"from nixtla.date_features import SpecialDates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"WARNING:nixtla.nixtla_client:You did not provide X_df. Exogenous variables in df are ignored. To surpress this warning, please add X_df with exogenous variables: christmas_vacations, summer_vacations\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Restricting input...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Create categorical variables to label Christmas and summer vacations\n",
"categories_dates = SpecialDates(\n",
" special_dates={\n",
" 'christmas_vacations': [datetime.date(year, 12, 1) for year in range(1949, 1960 + 1)],\n",
" 'summer_vacations': [datetime.date(year, month, 1) for year in range(1949, 1960 + 1) for month in (6, 7)]\n",
" }\n",
")\n",
"\n",
"dates = pd.date_range('1949-01-01', '1960-12-01', freq='MS')\n",
"\n",
"categories_df = categories_dates(dates).reset_index(drop=True)\n",
"\n",
"# Merge with the dataset\n",
"cat_df = pd.concat([df, categories_df], axis=1)\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=cat_df, \n",
" h=24,\n",
" target_col='value',\n",
" time_col='timestamp'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a detailed guide on using categorical variables for forecasting, read our in-depth tutorial on [Categorical variables](https://docs.nixtla.io/docs/tutorials-categorical_variables)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/05_longhorizon.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Long-horizon forecasting\n",
"\n",
"Long-horizon forecasting is when you wish to predict more than one seasonal cycle into the future. TimeGPT supports long-horizon forecasting simply by setting `model=timegpt-1-long-horizon`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/05_longhorizon.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/05_longhorizon')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Restricting input...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df,\n",
" h=36,\n",
" model='timegpt-1-long-horizon',\n",
" time_col='timestamp',\n",
" target_col=\"value\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a detailed guide on long-horizon forecasting, read our in-depth tutorial on [Long-horizon forecasting](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/06_multiple_series.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple series forecasting\n",
"\n",
"TimeGPT can concurrently forecast many series at the same time. Simply use a DataFrame with more than one unique value in the `unique_id` column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/06_multiple_series.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/06_multiple_series')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Inferred freq: H\n",
"INFO:nixtla.nixtla_client:Restricting input...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')\n",
"\n",
"# Forecast\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df, \n",
" h=24\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details on forecasting multiple series, read our in-depth tutorial on [Multiple series forecasting](https://docs.nixtla.io/docs/tutorials-multiple_series_forecasting)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/07_finetuning.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fine-tuning\n",
"\n",
"We can fine-tune TimeGPT by specifying the `finetune_steps` parameter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/07_finetuning')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Forecast with fine-tuning.\n",
"# Here, we fine-tune for 5 steps\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df,\n",
" h=12,\n",
" finetune_steps=5,\n",
" time_col='timestamp',\n",
" target_col=\"value\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, only a small amount of finetuning is applied (`finetune_depth=1`). We can increase the intensity of finetuning by increasing the `finetune_depth` parameter. Note that increasing `finetune_depth` and `finetune_steps` increases wall time for generating predictions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Forecast with fine-tuning.\n",
"# Here, we fine-tune for 5 steps\n",
"# and we finetune more than just the last layer\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df,\n",
" h=12,\n",
" finetune_steps=5,\n",
" finetune_depth=2,\n",
" time_col='timestamp',\n",
" target_col=\"value\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more information on fine-tuning, read our [fine-tuning tutorial](https://docs.nixtla.io/docs/tutorials-fine_tuning)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/08_custom_loss_function.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Finetuning with a custom loss function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When fine-tuning, we can specify a loss function to be used usin the `finetune_loss` argument.\n",
"\n",
"The possible values are:\n",
"\n",
"* `\"mae\"`\n",
"\n",
"* `\"mse\"`\n",
"\n",
"* `\"rmse\"`\n",
"\n",
"* `\"mape\"`\n",
"\n",
"* `\"smape\"`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/08_custom_loss_function.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/08_custom_loss_function')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
}
],
"source": [
"# Read data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Fine-tune with a specified loss function and make predictions\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df,\n",
" h=12,\n",
" finetune_steps=5,\n",
" finetune_loss=\"mae\",\n",
" time_col='timestamp',\n",
" target_col=\"value\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details on specifying a loss function and how it impacts the performance of the model, read our in-depth tutorial on [Fine-tuning with a specific loss function](https://docs.nixtla.io/docs/tutorials-fine_tuning_with_a_specific_loss_function)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/09_cross_validation.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cross validation\n",
"\n",
"We can perform cross-validation by simply using the `cross-validation` method. Specify the number of windows using the `n_windows` argument."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/09_cross_validation.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/09_cross_validation')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"INFO:nixtla.nixtla_client:Restricting input...\n",
"INFO:nixtla.nixtla_client:Calling Cross Validation Endpoint...\n"
]
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Cross-validation using two windows\n",
"forecast_cv_df = nixtla_client.cross_validation(\n",
" df=df,\n",
" h=12,\n",
" n_windows=2,\n",
" time_col='timestamp',\n",
" target_col=\"value\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details, check out our [cross-validation tutorial](https://docs.nixtla.io/docs/tutorials-cross_validation)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/10_prediction_intervals.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predictions intervals\n",
"\n",
"We can generate prediction intervals using the `level` parameter in the `forecast` method. It takes any values between 0 and 100, including decimal numbers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/forecast/10_prediction_intervals.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/forecast/10_prediction_intervals')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"INFO:nixtla.nixtla_client:Restricting input...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
},
{
"data": {
"image/png": 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AAAAAAAAAAAAAwMXR4AEAAAAAAAAAAAAAAODiaPAAAAAAAAAAAAAAAABwcf8Hbjx20ulUJXQAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 2400x350 with 1 Axes>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv\")\n",
"\n",
"# Forecast using a 80% confidence interval\n",
"forecast_df = nixtla_client.forecast(\n",
" df=df,\n",
" h=12,\n",
" time_col='timestamp',\n",
" target_col=\"value\",\n",
" level=[80]\n",
")\n",
"\n",
"# Plot predictions with intervals\n",
"nixtla_client.plot(\n",
" df=df, \n",
" forecasts_df=forecast_df, \n",
" time_col='timestamp', \n",
" target_col='value',\n",
" level=[80]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details on uncertainty quantification, read our tutorials on using [quantile forecasts](https://docs.nixtla.io/docs/tutorials-quantile_forecasts) and [prediction intervals](https://docs.nixtla.io/docs/tutorials-prediction_intervals)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/capabilities/forecast/11_irregular_timestamps.ipynb 0 → 100644
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nbs/docs/capabilities/historical-anomaly-detection/00_historical_anomaly_detection.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "markdown",
"id": "6de758ee-a0d2-4b3f-acff-eed419dd17c5",
"metadata": {},
"source": [
"# Historical anomaly detection"
]
},
{
"cell_type": "markdown",
"id": "5d267032-535b-4b7b-b7d3-d2db8f673af6",
"metadata": {},
"source": [
"This section provides various recipes for performing historical anomaly detection using TimeGPT.\n",
"\n",
"Historical anomaly detection identifies data points that deviate from the expected behavior over a given historical time series, helping to spot fraudulent activity, security breaches, or significant outliers.\n",
"\n",
"The process involves generating predictions and constructing a 99% confidence interval. Data points falling outside this interval are considered anomalies.\n",
"\n",
"This section covers:\n",
"\n",
"* [Historical anomaly detection](https://docs.nixtla.io/docs/capabilities-historical-anomaly-detection-quickstart)\n",
"\n",
"* [Historical anomaly detection with exogenous features](https://docs.nixtla.io/docs/capabilities-historical-anomaly-detection-add_exogenous_variables)\n",
"\n",
"* [Historical anomaly detection with date features](https://docs.nixtla.io/docs/capabilities-historical-anomaly-detection-add_date_features)\n",
"\n",
"* [Modifying the confidence intervals](https://docs.nixtla.io/docs/capabilities-historical-anomaly-detection-add_confidence_levels)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
nbs/docs/capabilities/historical-anomaly-detection/01_quickstart.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quickstart\n",
"\n",
"To perform historical anomaly detection, use the `detect_anomalies` method. Then, plot the anomalies using the `plot` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/anomaly-detection/01_quickstart.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/historical-anomaly-detection/01_quickstart')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, set the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1600x350 with 1 Axes>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read the dataset\n",
"df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/peyton-manning.csv')\n",
"\n",
"# Detect anomalies\n",
"anomalies_df = nixtla_client.detect_anomalies(df, freq='D')\n",
"\n",
"# Plot anomalies\n",
"nixtla_client.plot(df, anomalies_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you use an Azure AI endpoint, set `model=\"azureai\"`\n",
">\n",
"> `nixtla_client.detect_anomalies(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, two models are supported: `timegpt-1` and `timegpt-1-long-horizon`.\n",
"> \n",
"> By default, `timegpt-1` is used. See [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) for details on using `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For an in-depth guide on historical anomaly detection with TimeGPT, check out our [tutorial](https://docs.nixtla.io/docs/tutorials-anomaly_detection)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
nbs/docs/capabilities/historical-anomaly-detection/02_anomaly_exogenous.ipynb 0 → 100644
View file @ f42429f6
This source diff could not be displayed because it is too large. You can view the blob instead.
nbs/docs/capabilities/historical-anomaly-detection/03_anomaly_detection_date_features.ipynb 0 → 100644
View file @ f42429f6
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add date features\n",
"\n",
"If your dataset lacks exogenous variables, add date features to inform the model for historical anomaly detection. Use the `date_features` argument. Set it to `True` to extract all possible features, or pass a list of specific features to include."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/anomaly-detection/03_anomaly_detection_date_features.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/capabilities/historical-anomaly-detection/03_anomaly_detection_date_features')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, set the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Querying model metadata...\n",
"INFO:nixtla.nixtla_client:Using the following exogenous features: ['month_1.0', 'month_2.0', 'month_3.0', 'month_4.0', 'month_5.0', 'month_6.0', 'month_7.0', 'month_8.0', 'month_9.0', 'month_10.0', 'month_11.0', 'month_12.0', 'year_2007.0', 'year_2008.0', 'year_2009.0', 'year_2010.0', 'year_2011.0', 'year_2012.0', 'year_2013.0', 'year_2014.0', 'year_2015.0', 'year_2016.0']\n",
"INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1600x350 with 1 Axes>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read the data\n",
"df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/peyton-manning.csv')\n",
"\n",
"# Add date features for anomaly detection\n",
"# Here, we use date features at the month and year levels\n",
"anomalies_df_x = nixtla_client.detect_anomalies(\n",
" df,\n",
" freq='D', \n",
" date_features=['month', 'year'],\n",
" date_features_to_one_hot=True,\n",
" level=99.99,\n",
")\n",
"\n",
"# Plot anomalies\n",
"nixtla_client.plot(df, anomalies_df_x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='features'>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot weights of date features\n",
"nixtla_client.weights_x.plot.barh(x='features', y='weights')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you use an Azure AI endpoint, set `model=\"azureai\"`\n",
">\n",
"> `nixtla_client.detect_anomalies(..., model=\"azureai\")`\n",
">\n",
"> For the public API, two models are supported: `timegpt-1` and `timegpt-1-long-horizon`.\n",
"> \n",
"> By default, `timegpt-1` is used. See [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) for details on using `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details, check out our in-depth tutorial on [anomaly detection](https://docs.nixtla.io/docs/tutorials/anomaly_detection)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
nbs/docs/capabilities/historical-anomaly-detection/04_confidence_levels.ipynb 0 → 100644
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