Lära Gradient Boosting for Time Series | Classical ML Models for Time Series

Gradient boosting is an ensemble learning technique that builds models sequentially, with each new model correcting the errors of the previous ones. Unlike a single decision tree, which can be prone to overfitting or underfitting, gradient boosting combines many weak learners—typically shallow trees—into a strong predictive model. This approach is especially powerful for time series forecasting, where capturing subtle temporal patterns and non-linear relationships is crucial. Gradient boosting methods, such as scikit-learn's HistGradientBoostingRegressor, are robust to outliers and can handle complex feature interactions, making them well-suited for forecasting tasks when compared to single trees.


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import numpy as np
import pandas as pd
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# Create a synthetic time series (sinusoidal with noise)
np.random.seed(42)
n = 300
t = np.arange(n)
y = np.sin(0.04 * t) + np.random.normal(scale=0.3, size=n)
df = pd.DataFrame({'y': y})

# Create lagged features and rolling means
df['lag1'] = df['y'].shift(1)
df['lag2'] = df['y'].shift(2)
df['roll3'] = df['y'].rolling(window=3).mean().shift(1)
df['roll7'] = df['y'].rolling(window=7).mean().shift(1)
df = df.dropna()

X = df[['lag1', 'lag2', 'roll3', 'roll7']]
y = df['y']

tscv = TimeSeriesSplit(n_splits=5)
mse_scores = []

# For plotting last fold
last_train_idx, last_test_idx = None, None
last_predictions = None

for train_idx, test_idx in tscv.split(X):
    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
    y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
    
    model = HistGradientBoostingRegressor(max_iter=100)
    model.fit(X_train, y_train)
    
    preds = model.predict(X_test)
    mse = mean_squared_error(y_test, preds)
    mse_scores.append(mse)
    
    last_train_idx, last_test_idx = train_idx, test_idx
    last_predictions = preds

print("Mean MSE across folds:", np.mean(mse_scores))


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# Visualize
plt.figure(figsize=(14, 6))

# Actual values (full series)
plt.plot(y.index, y.values, label="Actual", color="black")

# Predictions on final fold
plt.plot(y.index[last_test_idx], last_predictions, 
         label="Predictions (Last Fold)", color="orange", linewidth=2)

# Train-test split line
plt.axvline(y.index[last_test_idx][0], linestyle="--", color="gray", label="Train/Test Split")

plt.title("HistGradientBoosting TS Forecast - Last Fold Visualization")
plt.xlabel("Time")
plt.ylabel("Value")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

When comparing random forests and gradient boosting for time series forecasting, several differences stand out:

Random forests average predictions from many independent trees;
This averaging reduces variance and improves robustness;
Some bias may remain if the underlying patterns are complex.

Gradient boosting takes a different approach:

Trees are built sequentially, with each tree correcting the errors of the previous one;
This process often leads to lower bias and better performance on challenging forecasting tasks;
Boosting models can be more sensitive to noise and overfitting if not tuned carefully.

Interpretability also differs:

Random forests are typically easier to analyze, as feature importances are more stable and the ensemble is less sensitive to small data changes;
Boosting models, while potentially more accurate, may require more careful examination to understand their predictions and feature dependencies.

1. What is a key advantage of gradient boosting over random forests for time series forecasting?

2. Which features are typically most important for boosting models in time series?

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import numpy as np
import pandas as pd
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# Create a synthetic time series (sinusoidal with noise)
np.random.seed(42)
n = 300
t = np.arange(n)
y = np.sin(0.04 * t) + np.random.normal(scale=0.3, size=n)
df = pd.DataFrame({'y': y})

# Create lagged features and rolling means
df['lag1'] = df['y'].shift(1)
df['lag2'] = df['y'].shift(2)
df['roll3'] = df['y'].rolling(window=3).mean().shift(1)
df['roll7'] = df['y'].rolling(window=7).mean().shift(1)
df = df.dropna()

X = df[['lag1', 'lag2', 'roll3', 'roll7']]
y = df['y']

tscv = TimeSeriesSplit(n_splits=5)
mse_scores = []

# For plotting last fold
last_train_idx, last_test_idx = None, None
last_predictions = None

for train_idx, test_idx in tscv.split(X):
    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
    y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
    
    model = HistGradientBoostingRegressor(max_iter=100)
    model.fit(X_train, y_train)
    
    preds = model.predict(X_test)
    mse = mean_squared_error(y_test, preds)
    mse_scores.append(mse)
    
    last_train_idx, last_test_idx = train_idx, test_idx
    last_predictions = preds

print("Mean MSE across folds:", np.mean(mse_scores))


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# Visualize
plt.figure(figsize=(14, 6))

# Actual values (full series)
plt.plot(y.index, y.values, label="Actual", color="black")

# Predictions on final fold
plt.plot(y.index[last_test_idx], last_predictions, 
         label="Predictions (Last Fold)", color="orange", linewidth=2)

# Train-test split line
plt.axvline(y.index[last_test_idx][0], linestyle="--", color="gray", label="Train/Test Split")

plt.title("HistGradientBoosting TS Forecast - Last Fold Visualization")
plt.xlabel("Time")
plt.ylabel("Value")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()