Darts: Why Specify Lags For Past & Future Covariates?

Nov 30, 2025 by Alex Johnson 54 views

Why Specify Lags for Past & Future Covariates in Darts Time Series?

When working with time series forecasting using the Darts library, you might encounter a situation where you need to specify lags for past and future covariates. This can seem a bit different from other machine learning libraries like Scikit-learn, where you typically just provide your feature matrix (X_train) and target variable (y_train). Let's dive into why this lag specification is necessary in Darts and how it contributes to the power and flexibility of the library.

Understanding the Role of Lags in Time Series Forecasting

In the realm of time series analysis, the concept of lags is fundamental. Simply put, a lag refers to a past value of a time series. For instance, a lag of 1 means "one time step ago," a lag of 2 means "two time steps ago," and so on. These lagged values often hold crucial information for predicting future values. Think about it: the stock market today is likely influenced by its performance yesterday, last week, and even further back. Similarly, sales figures this month might be related to advertising spending in the previous months.

When we talk about covariates (also known as exogenous variables or external factors), lags become even more interesting. Covariates are other time series that might influence our target series. For example, if we're forecasting electricity demand, weather data (temperature, humidity) would be relevant covariates. To effectively use these covariates, we need to consider not just their current values but also their past values – their lags. Specifying these lags essentially tells the model which historical values of the covariates are relevant for making predictions.

The need to specify lags in Darts stems from the nature of time series models themselves. Unlike standard regression models that treat observations as independent, time series models explicitly acknowledge the temporal dependence between data points. This dependence is captured through the use of lags. By specifying which lags of the covariates to include, we're providing the model with the information it needs to learn these temporal relationships.

Darts' Approach to Covariates and Lags

Darts is designed to handle time series data with a focus on flexibility and ease of use. It supports a wide range of models, from classical statistical models like ARIMA to powerful neural network models. When you introduce past and future covariates into your Darts models, the library requires you to be explicit about the lags you want to consider. This isn't an arbitrary requirement; it's a design choice that allows Darts to:

Model Temporal Dependencies Explicitly: By forcing you to define the lags, Darts ensures that the model is aware of the time relationships you believe are important. This leads to more interpretable and potentially more accurate models.
Handle Complex Relationships: You might have covariates that influence the target series in complex ways. For instance, a covariate might have a delayed effect, or its influence might wane over time. Specifying lags allows you to capture these nuances.
Optimize Model Performance: By carefully selecting the relevant lags, you can avoid including irrelevant information in the model, which can improve its performance and reduce overfitting.

In essence, Darts treats time series forecasting as a structured learning problem where the temporal relationships are explicitly modeled. This is in contrast to some other libraries that might implicitly handle lags or require you to manually create lagged features.

Contrasting with Scikit-learn

You mentioned the difference between Darts and Scikit-learn in terms of specifying lags. In Scikit-learn, you typically prepare your data by creating lagged features manually. For example, if you wanted to include a lag of 1 for a covariate, you would create a new column in your feature matrix that contains the lagged values. This approach works, but it places the burden of lag creation and management on you.

Darts, on the other hand, takes a more integrated approach. By allowing you to specify lags directly within the model definition, Darts handles the creation and management of lagged features internally. This simplifies the process and reduces the risk of errors. It also allows Darts to optimize the lag selection process, for example, by using techniques like cross-validation to determine the best lags to use.

Let's illustrate the difference with a simple example. Suppose you have a target series y and a covariate x, and you want to include lags 1 and 2 of the covariate in your model.

In Scikit-learn, you might do something like this:

import pandas as pd
from sklearn.linear_model import LinearRegression

# Sample data (replace with your actual data)
data = {
 'y': [1, 2, 3, 4, 5],
 'x': [6, 7, 8, 9, 10]
}
df = pd.DataFrame(data)

# Create lagged features
df['x_lag1'] = df['x'].shift(1)
df['x_lag2'] = df['x'].shift(2)

# Drop rows with NaN values (due to shifting)
df = df.dropna()

# Prepare data for Scikit-learn
X = df[['x', 'x_lag1', 'x_lag2']]
y = df['y']

# Train a linear regression model
model = LinearRegression()
model.fit(X, y)

# Make predictions
# ...

In Darts, the equivalent would be:

from darts import TimeSeries
from darts.models import LinearRegressionModel

# Sample data (replace with your actual data)
data = {
 'y': [1, 2, 3, 4, 5],
 'x': [6, 7, 8, 9, 10]
}
df = pd.DataFrame(data)

# Create TimeSeries objects
y_series = TimeSeries.from_dataframe(df, time_col=None, value_cols=['y'])
x_series = TimeSeries.from_dataframe(df, time_col=None, value_cols=['x'])

# Define the model with lags
model = LinearRegressionModel(lags_past_covariates=[1, 2])

# Train the model
model.fit(y_series, past_covariates=x_series)

# Make predictions
# ...

Notice how in the Darts example, you directly specify the lags when creating the model. Darts then handles the creation of the lagged features internally. This makes the code cleaner and more focused on the modeling aspects.

Practical Implications and Best Practices

So, you understand why you need to specify lags in Darts, but how do you decide which lags to use? Here are some practical considerations and best practices:

Domain Knowledge: Your understanding of the underlying system can be invaluable. If you know, for example, that there's a one-month delay between an advertising campaign and its impact on sales, you would naturally include a lag of one month for the advertising spend covariate.
Autocorrelation and Cross-correlation: Analyzing the autocorrelation of your target series and the cross-correlation between your target series and covariates can provide clues about relevant lags. Autocorrelation measures the correlation of a time series with its own past values, while cross-correlation measures the correlation between two time series at different lags.
Trial and Error: Sometimes, the best approach is to experiment with different lag configurations and evaluate their impact on model performance. You can use techniques like cross-validation to compare models with different lag settings.
Regularization Techniques: If you're unsure about which lags are most important, you can use regularization techniques (like L1 or L2 regularization) within your model. These techniques can help to automatically shrink the coefficients of less important lags, effectively performing feature selection.
Start with a Range: If you are unsure about the specific lags, start with a broader range and progressively refine based on model performance. For instance, you might initially try lags from 1 to 12 and then narrow down the range based on cross-validation results.

Conclusion: Embracing the Power of Lags in Darts

Specifying lags for past and future covariates in Darts might seem like an extra step compared to some other libraries, but it's a crucial aspect of the library's design. It allows you to explicitly model the temporal relationships in your data, leading to more accurate and interpretable forecasts. By understanding the role of lags and how to select them effectively, you can harness the full power of Darts for your time series forecasting tasks.

By taking the time to carefully consider and specify the relevant lags, you're setting your models up for success and ensuring that they capture the intricate patterns and dependencies within your time series data. This approach not only improves forecasting accuracy but also provides valuable insights into the dynamics of the system you're modeling. Embrace the power of lags, and you'll be well on your way to mastering time series forecasting with Darts.

For further exploration on time series analysis and forecasting, consider visiting reputable resources such as the statsmodels documentation, a comprehensive Python library for statistical modeling, including time series analysis. This will give you a broader understanding of the statistical underpinnings of time series methods.