utilsforecast

import pandas as pd import numpy as np from utilsforecast.data import generate_series from utilsforecast.preprocessing import fill_gaps from utilsforecast.evaluation import evaluate from utilsforecast.losses import mape, mse from functools import partial # 1. Generate synthetic data with some missing values series = generate_series(n_series=3, max_length=50, equal_ends=True, seed=42) # Introduce some gaps for demonstration mis_idx = np.random.choice(series.index, size=int(len(series) * 0.1), replace=False) series_with_gaps = series.drop(mis_idx) # 2. Fill missing values filled_series = fill_gaps(series_with_gaps, freq='D') print("Original series head (with gaps if any):") print(series_with_gaps.head()) print("\nFilled series head:") print(filled_series.head()) # 3. Prepare data for evaluation (example with dummy models) # Assume 'y_true' is the actual target and 'model1', 'model2' are predictions # For demonstration, we'll create a dummy 'y' and then 'predictions' filled_series['y_true'] = filled_series['y'] filled_series['model1'] = filled_series['y_true'] * np.random.uniform(0.9, 1.1, len(filled_series)) filled_series['model2'] = filled_series['y_true'] * np.random.uniform(0.8, 1.2, len(filled_series)) # Split into train and validation (simplified for quickstart) horizon = 7 valid_df = filled_series.groupby('unique_id').tail(horizon).copy() train_df = filled_series.drop(valid_df.index).copy() # Ensure target column for evaluation matches original 'y' valid_df['y'] = valid_df['y_true'] train_df['y'] = train_df['y_true'] # 4. Evaluate dummy models # Mase requires a training set to compute the scaling factor dummy_mase = partial(mse, seasonality=1) metrics_df = evaluate(valid_df, metrics=[mape, dummy_mase], train_df=train_df) print("\nEvaluation Results:") print(metrics_df.head())