Sliding Window Approach to Time Series Cross-Validation

Time series cross-validation evaluates a model’s predictive performance by training on past data and testing on subsequent time periods using a sliding window approach.

MLForecast offers an efficient and easy-to-use implementation of this technique.

To see how to implement time series cross-validation with MLForecast, let’s start reading a subset of the M4 Competition hourly dataset.

import pandas as pd
from utilsforecast.plotting import plot_series

Y_df = pd.read_csv("https://datasets-nixtla.s3.amazonaws.com/m4-hourly.csv").query(
    "unique_id == 'H1'"
)
Y_df

    unique_id   ds      y
0          H1    1  605.0
1          H1    2  586.0
2          H1    3  586.0
3          H1    4  559.0
4          H1    5  511.0
..        ...  ...    ...
743        H1  744  785.0
744        H1  745  756.0
745        H1  746  719.0
746        H1  747  703.0
747        H1  748  659.0

[748 rows x 3 columns]

Plot the time series:

fig = plot_series(Y_df, plot_random=False, max_insample_length=24 * 14)
fig

Instantiate a new MLForecast object:

from mlforecast import MLForecast
from mlforecast.target_transforms import Differences
from sklearn.linear_model import LinearRegression


mlf = MLForecast(
    models=[LinearRegression()],
    freq=1,
    target_transforms=[Differences([24])],
    lags=range(1, 25),
)

Once the MLForecast object has been instantiated, we can use the cross_validation method.

For this particular example, we’ll use 3 windows of 24 hours.

# use 3 windows of 24 hours
cross_validation_df = mlf.cross_validation(
    df=Y_df,
    h=24,
    n_windows=3,
)
cross_validation_df.head()

  unique_id   ds  cutoff      y  LinearRegression
0        H1  677     676  691.0        676.726797
1        H1  678     676  618.0        559.559522
2        H1  679     676  563.0        549.167938
3        H1  680     676  529.0        505.930997
4        H1  681     676  504.0        481.981893

We’ll now plot the forecast for each cutoff period.

import matplotlib.pyplot as plt


def plot_cv(df, df_cv, last_n=24 * 14):
    cutoffs = df_cv["cutoff"].unique()
    fig, ax = plt.subplots(
        nrows=len(cutoffs), ncols=1, figsize=(14, 6), gridspec_kw=dict(hspace=0.8)
    )
    for cutoff, axi in zip(cutoffs, ax.flat):
        df.tail(last_n).set_index("ds").plot(ax=axi, y="y")
        df_cv.query("cutoff == @cutoff").set_index("ds").plot(
            ax=axi,
            y="LinearRegression",
            title=f"{cutoff=}",
        )


plot_cv(Y_df, cross_validation_df)