---
title: "Date/Time Features"
description: "Learn how to incorporate date/time features into your forecasts to improve performance."
icon: "clock"
---
## Why incorporate Date/Time Features in your Forecasts
Many time series display patterns that repeat based on the calendar like demand
increasing on weekends, sales peaking at the end of the month, or traffic
varying by hour of the day. Recognizing and capturing these time-based patterns
can be a powerful way to improve forecasting accuracy.
While you can forecast a time series based solely on its historical values,
adding additional date/time related features, such as the day of the
week, month, quarter, or hour, can often enhance the model's performance. These
features can be especially useful when your dataset lacks exogenous variables,
but they can also complement external regressors when available.
In this tutorial, we'll walk through how to incorporate these date/time features
into TimeGPT to boost the accuracy of your forecasts.
## How to incorporate Date/Time Features in your Forecasts
[](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/date_features.ipynb)
### Step 1: Import Packages
Import the necessary libraries and initialize the Nixtla client.
```python
import numpy as np
import pandas as pd
from nixtla import NixtlaClient
# For forecast evaluation
from utilsforecast.evaluation import evaluate
from utilsforecast.losses import mae, rmse
```
You can instantiate the `NixtlaClient` class providing your authentication API key.
```python
nixtla_client = NixtlaClient(
# defaults to os.environ.get("NIXTLA_API_KEY")
api_key='my_api_key_provided_by_nixtla'
)
```
### Step 2: Load Data
In this notebook, we use hourly electricity prices as our example dataset, which
consists of 5 time series, each with approximately 1700 data points. For
demonstration purposes, we focus on the German electricity price series. The
time series is split, with the last 240 steps (10 days) set aside as the test set.
For simplicity, we will also demonstrate this tutorial without the use of any
additional exogenous variables, but you could extend this same technique for
datasets that have exogenous variables.
```python
df = pd.read_csv(
'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv'
)
df['ds'] = pd.to_datetime(df['ds'])
df_sub = df.query('unique_id == "DE"')[['unique_id','ds','y']]
```
```python
df_train = df_sub.query('ds < "2017-12-21"')
df_test = df_sub.query('ds >= "2017-12-21"')
df_train.shape, df_test.shape
```
```bash
((1440, 3), (240, 3))
```
```python
nixtla_client.plot(df_train, df_test.rename(columns={'y': 'test'}))
```
### Step 3: Forecasting
#### Without Datetime Features
First, we forecast the univariate time series without the use of datetime
features.
```python
fcst_timegpt_no_dt = nixtla_client.forecast(
df = df_train,
h=24*10,
model="timegpt-1-long-horizon"
)
```
We will rename the forecast column for this approach, so that we can distinguish
it from forecasts created using other methods later.
```python
fcst_timegpt_no_dt.rename(columns={"TimeGPT": "TimeGPT_no_dt"}, inplace=True)
```
#### With Inbuilt Datetime Features
Next, let's forecast the same univariate time series with datetime features.
This can be done by specifying the `date_features` argument. The
data is hourly, so both the hour of the day (`hour`) and the day of
the week (`dayofweek`) may impact the usage.
For example, the usage may peak in the afternoon and drop off at night. It can
also differ between the weekdays and weekends due to working and holiday
patterns. Including these features can help the model make better forecasts.
> NOTE:
> 1. In order to show how these features are created, we can add the
`feature_contribution` agrument. This is just for demonstration purposes in this
tutorial and not truly needed to forecast with datetime features.
> 2. If you have a weekly frequency dataset, you can use
`date_features = ["week", "month", "year"]` or a subset of these features.
> 3. If you have a monthly frequency dataset, you can use
`date_features = ["month", "year"]` or a subset of these features.
```python
fcst_timegpt_dt_no_ohe = nixtla_client.forecast(
df = df_train,
h=24*10,
model="timegpt-1-long-horizon",
date_features=['hour', 'dayofweek'],
feature_contributions=True
)
```
```python
shap_df = nixtla_client.feature_contributions
shap_df.head()
```
| | unique_id | ds | TimeGPT | hour | dayofweek | base_value |
|--:|----------:|--------------------:|----------:|-----------:|----------:|-----------:|
| 0 | DE | 2017-12-21 00:00:00 | 34.945976 | -12.797431 | 4.236599 | 43.506810 |
| 1 | DE | 2017-12-21 01:00:00 | 33.700954 | -14.274811 | 4.168986 | 43.806778 |
| 2 | DE | 2017-12-21 02:00:00 | 32.120293 | -15.785894 | 4.123096 | 43.783092 |
| 3 | DE | 2017-12-21 03:00:00 | 32.544914 | -15.623017 | 4.542475 | 43.625454 |
| 4 | DE | 2017-12-21 04:00:00 | 33.698105 | -14.559433 | 4.525819 | 43.731720 |
As we can see, two new exogenous features (`hour` and `dayofweek`) got added to
the dataset and the forecast utilized these features.
However, we need to ensure that the model treats each hour (0, 1, 2, ..., 23)
and each day (0, 1, 2, ..., 6) as a categorical variable and not as a numerical
variable. If treated numerically, the model may exaggerate differences (e.g.,
hour 23 might appear 23 times more influential than hour 1), which doesn't
reflect real patterns. Electricity usage at hour 23 is typically similar to
hour 1, and day 6 usage often resembles day 0.
To avoid this distortion, we one-hot encode these variables using the
`date_features_to_one_hot` argument. This creates a separate exogenous feature
for each hour and each day, allowing the model to capture their effects
independently.
```python
fcst_timegpt_dt = nixtla_client.forecast(
df = df_train,
h=24*10,
model="timegpt-1-long-horizon",
date_features=['hour', 'dayofweek'],
date_features_to_one_hot=['hour', 'dayofweek'],
feature_contributions=True
)
```
```python
shap_df = nixtla_client.feature_contributions
shap_df.head()
```
| | unique_id | ds | TimeGPT | hour_0 | hour_1 | hour_2 | hour_3 | hour_4 | hour_5 | hour_6 | ... | hour_22 | hour_23 | dayofweek_0 | dayofweek_1 | dayofweek_2 | dayofweek_3 | dayofweek_4 | dayofweek_5 | dayofweek_6 | base_value | |
|--:|----------:|---:|--------------------:|----------:|-----------:|-----------:|-----------:|-----------:|-----------:|---------:|---------:|--------:|---------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|-----------:|-----------|
| 0 | 0 | DE | 2017-12-21 00:00:00 | 35.248108 | -13.396377 | 0.387143 | 0.423001 | 0.392672 | 0.373034 | 0.333778 | 0.147671 | ... | 0.271507 | 0.393282 | 0.472389 | -0.377321 | -0.548429 | -0.101086 | -0.133001 | 1.455560 | 2.975230 | 44.333805 |
| 1 | 1 | DE | 2017-12-21 01:00:00 | 34.400800 | 0.358443 | -14.488875 | 0.389985 | 0.359990 | 0.341219 | 0.320964 | 0.135058 | ... | 0.266497 | 0.391259 | 0.445456 | -0.306117 | -0.436959 | -0.172850 | -0.151865 | 1.533456 | 3.022358 | 44.539093 |
| 2 | 2 | DE | 2017-12-21 02:00:00 | 33.175526 | 0.375983 | 0.372809 | -15.824338 | 0.348533 | 0.351379 | 0.317832 | 0.123833 | ... | 0.273698 | 0.410714 | 0.417348 | -0.279551 | -0.342991 | -0.171547 | -0.142890 | 1.532721 | 3.042772 | 44.515614 |
| 3 | 3 | DE | 2017-12-21 03:00:00 | 33.205390 | 0.368333 | 0.366936 | 0.372584 | -15.880591 | 0.346306 | 0.319877 | 0.136488 | ... | 0.276705 | 0.416273 | 0.508190 | -0.274014 | -0.339005 | -0.176228 | -0.152890 | 1.588364 | 3.095226 | 44.391410 |
| 4 | 4 | DE | 2017-12-21 04:00:00 | 34.689583 | 0.363581 | 0.363459 | 0.393807 | 0.362043 | -14.755774 | 0.314718 | 0.141911 | ... | 0.274819 | 0.402653 | 0.531417 | -0.277548 | -0.360688 | -0.159342 | -0.169762 | 1.692538 | 3.165733 | 44.505848 |
As we can see above, this now creates a separate feature for each hour of the
day and each day of the week.
> NOTE: With one hot encoding, the number of features can increase by a lot.
This is especially true if you have weekly frequency data and you are using
`date_feature=["week"]` because this leads to 52 features being created after
one hot encoding. Please make sure that your dataset has enough datapoints or
else the model will overfit to the data. You can increase the number of
datapoints in the dataset by increasing the available history for your time
series, or increasing the number of unique time series that share a common
pattern in your dataset.
```python
fcst_timegpt_dt.rename(columns={"TimeGPT": "fcst_timegpt_dt"}, inplace=True)
```
#### With Custom Datetime Features
In the example above, we saw how to incorporate the inbuilt datetime features
into the forecast. However, as seen above, in some cases, it may not be feasible
to one hot encode the datetime features since it may lead to a large number of
features for the dataset size. In that case, we can create a custom datetime
feature and use it in the forecast.
In this example, we will create a sine/cosine encoder for the week which is a
popular technique to encode datetime features due to their circular nature
described above (e.g. hour 23 behavior is close to hour 0 behavior, week 52
behavior is very close to week 1 behavior, etc.).
```python
class SinCosWeekOfYear:
"""
Adds sine and cosine features for each week of the year. This is useful for
models that can benefit from understanding the periodicity of weeks in a year.
"""
def __call__(self, dates: pd.DatetimeIndex):
df = pd.DataFrame(index=dates)
# Get week of year (1 to 53)
weeks = np.array([date.isocalendar().week for date in dates])
# Calculate sine and cosine features
df["week_sin"] = np.sin((2 * np.pi) * (weeks-1) / 53).round(4)
df["week_cos"] = np.cos((2 * np.pi) * (weeks-1) / 53).round(4)
return df
def __name__(self):
return "SinCosWeekOfYear"
# Example usage
dates = pd.date_range(start='2023-01-01', periods=55, freq='W-MON')
sin_cos_week = SinCosWeekOfYear()
features = sin_cos_week(dates)
features.tail()
```
| | week_sin | week_cos |
|-----------:|---------:|---------:|
| 2023-12-18 | -0.3482 | 0.9374 |
| 2023-12-25 | -0.2349 | 0.9720 |
| 2024-01-01 | 0.0000 | 1.0000 |
| 2024-01-08 | 0.1183 | 0.9930 |
| 2024-01-15 | 0.2349 | 0.9720 |
As we can see above, because of the cyclical encoding of the datetime feature,
the encoded values (`week_sin` and `week_cos`) for week 2023-12-25 (week 52)
is very close to 2024-01-01 (week 1). This will ensure that the learned features
for week 52 will be close to those for week 1. This has also helped us get the
feature cardinality down from 53 (in case of one hot encoding) to only 2 features.
In our example, we have the hour feature wich has a relatively high cardinality
after one hot encoding. Let's encode this with sine and cosine features and use
this instead of the one hot encoding.
```python
class SinCosHourOfDay:
"""
Adds sine and cosine features for each hour of the day. This is useful for
models that can benefit from understanding the periodicity of hours in a day.
"""
def __call__(self, dates: pd.DatetimeIndex):
df = pd.DataFrame(index=dates)
# Get hour of day (0 to 23)
hours = np.array([date.hour for date in dates])
# Calculate sine and cosine features
df["hour_sin"] = np.sin((2 * np.pi) * (hours) / 24).round(4)
df["hour_cos"] = np.cos((2 * np.pi) * (hours) / 24).round(4)
return df
def __name__(self):
return "SinCosHourOfDay"
# Example usage
dates = pd.date_range(start='2023-01-01 00:00', periods=26, freq='h')
sin_cos_hour = SinCosHourOfDay()
features = sin_cos_hour(dates)
features.tail()
```
| | hour_sin | hour_cos |
|--------------------:|---------:|---------:|
| 2023-01-01 21:00:00 | -0.7071 | 0.7071 |
| 2023-01-01 22:00:00 | -0.5000 | 0.8660 |
| 2023-01-01 23:00:00 | -0.2588 | 0.9659 |
| 2023-01-02 00:00:00 | 0.0000 | 1.0000 |
| 2023-01-02 01:00:00 | 0.2588 | 0.9659 |
In order to use this custom datetime feature, we can simply pass an instance of
the class to the `date_features` argument. Since this is alreay encoded, we do
not need to include it in the `date_features_to_one_hot` argument.
```python
fcst_timegpt_dt_custom = nixtla_client.forecast(
df = df_train,
h=24*10,
model="timegpt-1-long-horizon",
date_features=[SinCosHourOfDay(), 'dayofweek'],
date_features_to_one_hot=['dayofweek'],
feature_contributions=True
)
```
```python
shap_df = nixtla_client.feature_contributions
shap_df.head()
```
| | unique_id | ds | TimeGPT | hour_sin | hour_cos | dayofweek_0 | dayofweek_1 | dayofweek_2 | dayofweek_3 | dayofweek_4 | dayofweek_5 | dayofweek_6 | base_value |
|--:|----------:|--------------------:|----------:|----------:|-----------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|-----------:|
| 0 | DE | 2017-12-21 00:00:00 | 35.801600 | -3.609636 | -9.003666 | 0.805974 | -0.424078 | -0.343238 | -0.428668 | -0.055370 | 1.462214 | 3.295479 | 44.102590 |
| 1 | DE | 2017-12-21 01:00:00 | 34.419390 | -3.824628 | -10.493365 | 0.714771 | -0.400898 | -0.282606 | -0.331269 | -0.115753 | 1.539153 | 3.245723 | 44.368263 |
| 2 | DE | 2017-12-21 02:00:00 | 32.892105 | -4.959243 | -10.772224 | 0.712402 | -0.439891 | -0.261654 | -0.207954 | -0.191223 | 1.481960 | 3.206257 | 44.323673 |
| 3 | DE | 2017-12-21 03:00:00 | 32.727295 | -5.161374 | -10.812295 | 0.771099 | -0.417504 | -0.262543 | -0.146066 | -0.258350 | 1.578070 | 3.268950 | 44.167310 |
| 4 | DE | 2017-12-21 04:00:00 | 34.121994 | -3.687167 | -11.353230 | 0.846524 | -0.387008 | -0.278475 | -0.169525 | -0.255498 | 1.788180 | 3.362950 | 44.255240 |
As we can see above, the hour has now gotten encoded using the sine and cosine
features instead of the one hot encoding.
```python
fcst_timegpt_dt_custom.rename(columns={"TimeGPT": "fcst_timegpt_dt_custom"}, inplace=True)
```
### Step 4: Compare Results
#### Visual Comparison
Let's compare the results visually first. For this, we will merge all the
forecasts together. This is why we had renamed the forecast columns above so
that we can distinguish the forecasts generated by the different methods.
```python
all_fcst = (
fcst_timegpt_no_dt
.merge(fcst_timegpt_dt, on=['unique_id', 'ds'])
.merge(fcst_timegpt_dt_custom, on=['unique_id', 'ds'])
)
all_fcst.head()
```
| | unique_id | ds | TimeGPT_no_dt | fcst_timegpt_dt | fcst_timegpt_dt_custom |
|--:|----------:|--------------------:|--------------:|----------------:|-----------------------:|
| 0 | DE | 2017-12-21 00:00:00 | 34.340740 | 35.248108 | 35.801600 |
| 1 | DE | 2017-12-21 01:00:00 | 34.376488 | 34.400800 | 34.419390 |
| 2 | DE | 2017-12-21 02:00:00 | 32.215570 | 33.175526 | 32.892105 |
| 3 | DE | 2017-12-21 03:00:00 | 34.485695 | 33.205390 | 32.727295 |
| 4 | DE | 2017-12-21 04:00:00 | 34.359673 | 34.689583 | 34.121994 |
```python
nixtla_client.plot(df_sub, all_fcst)
```
Visually looking at the results shows that the forecast with the datetime
features is closer to the actuals as compared to the forecast without the
datetime features.
#### Metric Comparison
Next, let's compare the forecast with the actual data quantitatively. We will
use two common metrics - `MAE` and `RMSE` for this purpose.
```python
all_fcst_with_actuals = (
df_test[["unique_id", "ds", "y"]]
.merge(all_fcst, on=['unique_id', 'ds'])
)
all_fcst_with_actuals.head()
```
| | unique_id | ds | y | TimeGPT_no_dt | fcst_timegpt_dt | fcst_timegpt_dt_custom |
|--:|----------:|--------------------:|------:|--------------:|----------------:|-----------------------:|
| 0 | DE | 2017-12-21 00:00:00 | 33.09 | 34.340740 | 35.248108 | 35.801600 |
| 1 | DE | 2017-12-21 01:00:00 | 35.26 | 34.376488 | 34.400800 | 34.419390 |
| 2 | DE | 2017-12-21 02:00:00 | 31.88 | 32.215570 | 33.175526 | 32.892105 |
| 3 | DE | 2017-12-21 03:00:00 | 33.04 | 34.485695 | 33.205390 | 32.727295 |
| 4 | DE | 2017-12-21 04:00:00 | 33.60 | 34.359673 | 34.689583 | 34.121994 |
```python
metrics = [mae, rmse]
evaluation = evaluate(
all_fcst_with_actuals,
metrics=metrics,
)
evaluation
```
| | unique_id | metric | TimeGPT_no_dt | fcst_timegpt_dt | fcst_timegpt_dt_custom |
|--:|----------:|-------:|--------------:|----------------:|-----------------------:|
| 0 | DE | mae | 27.527012 | 21.644545 | 21.139603 |
| 1 | DE | rmse | 33.478168 | 28.099654 | 27.616988 |
As we can see, the addition of the datetime features improved the forecasting
metrics compared to the baseline model created without these features.
## Conclusion
As demonstrated in this tutorial
1. Providing datetime features to the model during forecasting can improve the
metrics substantially.
2. However, users must be careful of the cardinality of the features after
datetime features have been added. If the feature cardinality is too large for
the dataset, it may lead to overfitting.
3. In case of high cardinality, users may consider a custom encoding approach
as demonstrated.