Forecasting Single Series

Forecasting a Single Time Series

When to Use

Use this workflow when you have one time series and want to predict its future values.

• ForecasterRecursive: Default choice. Uses its own predictions as inputs for multi-step forecasting. Works with any sklearn-compatible regressor.
• ForecasterDirect: Trains one model per forecast step. Better when the relationship between lags and target changes across the horizon.

Complete Workflow

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from skforecast.recursive import ForecasterRecursive
from skforecast.preprocessing import RollingFeatures
from skforecast.model_selection import backtesting_forecaster, TimeSeriesFold

# 1. Load and prepare data (MUST have DatetimeIndex with frequency)
data = pd.read_csv('data.csv', index_col='date', parse_dates=True)
data = data.asfreq('h')  # Set frequency — required

# 2. Train/test split (time-based, never random)
end_train = '2023-01-01'
y_train = data.loc[:end_train, 'target']
y_test = data.loc[end_train:, 'target']

# 3. Create forecaster with optional rolling features
rolling_features = RollingFeatures(
    stats=['mean', 'std'],
    window_sizes=24
)

forecaster = ForecasterRecursive(
    estimator=RandomForestRegressor(n_estimators=100, random_state=123),
    lags=24,
    window_features=rolling_features,
    transformer_y=None,          # e.g., StandardScaler() for scaling
    differentiation=None,        # e.g., 1 for first-order differencing
)

# 4. Train
forecaster.fit(y=y_train)

# 5. Predict
predictions = forecaster.predict(steps=10)

# 6. Backtesting (proper evaluation)
cv = TimeSeriesFold(
    steps=10,
    initial_train_size=len(y_train),
    refit=False,
    fixed_train_size=False,
)

metric, predictions_bt = backtesting_forecaster(
    forecaster=forecaster,
    y=data['target'],
    cv=cv,
    metric='mean_absolute_error',
)
print(f"MAE: {metric}")

# 7. Prediction intervals
# Default interval is [5, 95] (90%). Here [10, 90] creates an 80% interval.
forecaster.fit(y=y_train, store_in_sample_residuals=True)
predictions_interval = forecaster.predict_interval(
    steps=10,
    interval=[10, 90],          # 80% prediction interval
    method='bootstrapping',
    n_boot=500,
)