capabilities-forecast-quickstart.mdx

---
title: "Forecasting Quickstart"
description: "Get started quickly with TimeGPT forecasting using the Nixtla API."
icon: "rocket"
---

[![Open In Colab](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)

# Quickstart

TimeGPT makes forecasting straightforward with the `forecast` method in the Nixtla API. Pass in your DataFrame, specify the time and target columns, and call `forecast`. You can also visualize results with the `plot` method.

<Info>
  Detailed guidance on data requirements is available [here](https://docs.nixtla.io/docs/getting-started-data_requirements).
</Info>


<Steps>

<Step title="1. Install & Import Dependencies">
  Make sure you have the latest Nixtla Client installed, then import the required libraries:

```bash Nixtla Client Installation
pip install nixtla
```

```python Import Libraries
import pandas as pd
from nixtla import NixtlaClient
```
</Step>

<Step title="2. Initialize the Nixtla Client">
  <Tabs>
    <Tab title="Standard Usage">
      <Check>
        Provide your API key from Nixtla to authenticate:
      </Check>

```python Nixtla Client Standard Initialization
nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key='my_api_key_provided_by_nixtla'
)
```
    </Tab>
    <Tab title="Using Azure AI Endpoint">
      <Check>
        Use an Azure AI endpoint<br/>
        If you'd like to use Azure AI, set the `base_url` to your Azure endpoint:
      </Check>

```python Nixtla Client Azure AI Endpoint
nixtla_client = NixtlaClient(
    base_url="your azure ai endpoint",
    api_key="your api_key"
)
```
    </Tab>
  </Tabs>
</Step>

<Step title="3. Load Data & Create Forecast">

```python Load Data and Run Forecast
# Read data
df = pd.read_csv("https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv")

# Forecast for the next 12 time steps
forecast_df = nixtla_client.forecast(
    df=df,
    h=12,
    time_col='timestamp',
    target_col="value"
)
```
</Step>

<Step title="4. Visualize Predictions">

```python Plot Forecast Results
# Plot predictions
nixtla_client.plot(
    df=df,
    forecasts_df=forecast_df,
    time_col='timestamp',
    target_col='value'
)
```
</Step>

</Steps>

<Step title="Sample Logs (Optional)">
  Below is an example of log output when running a forecast:

<Accordion title="Forecast Log Output">

```bash Forecast Process Logs
INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: MS
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
```

</Accordion>
</Step>

<Frame caption="TimeGPT Forecast Plot">
  ![Forecast Plot](https://raw.githubusercontent.com/Nixtla/nixtla/readme_docs/nbs/_docs/docs/capabilities/forecast/01_quickstart_files/figure-markdown_strict/cell-10-output-2.png)
</Frame>

<Info>
  **Available models in Azure AI**<br/>
  To use an Azure AI endpoint for anomaly detection, set the `model` parameter to `"azureai"`:

```python Azure AI Anomaly Detection
nixtla_client.detect_anomalies(
    ...,
    model="azureai"
)
```
</Info>

<CardGroup cols={2}>
  <Card title="timegpt-1">
    Default option for general forecasting needs.
  </Card>
  <Card title="timegpt-1-long-horizon">
    Optimized for extended forecast horizons. [Learn more here](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting).
  </Card>
</CardGroup>