This example demonstrates how to evaluate an XGBoost model for time series forecasting using `TimeSeriesSplit`

cross-validation, highlighting the importance of using time-aware splitting for model evaluation in time series tasks.

The `TimeSeriesSplit`

class from scikit-learn allows us to evaluate our XGBoost model using walk-forward validation, where the model is repeatedly fit on the past data and evaluated interval predictions.

We’ll use a synthetic dataset for simplicity and reproducibility.

```
# XGBoosting.com
# Evaluate XGBoost for Time Series Forecasting with TimeSeriesSplit
import numpy as np
from xgboost import XGBRegressor
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_squared_error
# Generate a synthetic univariate time series dataset
series = np.sin(0.1 * np.arange(200)) + np.random.randn(200) * 0.1
# Prepare the data for supervised learning
X, y = [], []
for i in range(10, len(series)):
X.append(series[i-10:i])
y.append(series[i])
X, y = np.array(X), np.array(y)
# Initialize TimeSeriesSplit for time-aware cross-validation
tscv = TimeSeriesSplit(n_splits=5)
# Initialize an XGBRegressor model
model = XGBRegressor(n_estimators=100, learning_rate=0.1, random_state=42)
# Evaluate the model using TimeSeriesSplit
mse_scores = []
for train_index, test_index in tscv.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
mse_scores.append(mse)
# Print the average performance across all splits
print(f"Average Mean Squared Error: {np.mean(mse_scores):.4f}")
```

This example focuses on evaluating an XGBoost model for time series forecasting using `TimeSeriesSplit`

cross-validation. Here’s a step-by-step breakdown:

- Generate a synthetic univariate time series dataset using a sine wave with added noise.
- Prepare the data for supervised learning by creating lagged features (here, we use the previous 10 time steps as features).
- Initialize
`TimeSeriesSplit`

for time-aware cross-validation with 5 splits. - Initialize an
`XGBRegressor`

model with chosen hyperparameters. - Evaluate the model using
`TimeSeriesSplit`

by iterating over the splits, fitting the model on the training data, making predictions on the test data, and calculating the Mean Squared Error (MSE) for each split. - Print the average MSE across all splits to assess the model’s overall performance.

Using `TimeSeriesSplit`

ensures that the model is evaluated on data that comes chronologically after the training data, mimicking a real-world scenario where future data is not available during training. This helps to assess the model’s ability to generalize to new, unseen data in a time series context.