Python Holt-Winters Demand Forecast Script Generator (pandas and statsmodels)

pip install pandas numpy statsmodels

import numpy as np
import pandas as pd
from statsmodels.tsa.holtwinters import ExponentialSmoothing

SEASON = 12
HORIZON = 6
HOLDOUT = 6

df = pd.read_csv("demand_history.csv", parse_dates=["month_start"])
series = df.set_index("month_start")["qty"].sort_index().asfreq("MS").fillna(0.0)

if len(series) < 2 * SEASON + HOLDOUT:
    raise SystemExit("Need at least two full seasons plus the holdout of history")

def fit(train):
    model = ExponentialSmoothing(
        train,
        trend="add",
        seasonal="add",
        seasonal_periods=SEASON,
    )
    return model.fit()

def wmape(actual, forecast):
    a = np.asarray(actual, dtype=float)
    f = np.asarray(forecast, dtype=float)
    return np.abs(a - f).sum() / a.sum() if a.sum() else float("nan")

train = series.iloc[:-HOLDOUT]
test = series.iloc[-HOLDOUT:]
hw_test = fit(train).forecast(HOLDOUT)
naive_test = series.shift(SEASON).iloc[-HOLDOUT:]

print("Holt-Winters WMAPE:", round(wmape(test, hw_test), 3))
print("Seasonal naive WMAPE:", round(wmape(test, naive_test), 3))

final = fit(series).forecast(HORIZON)
out = final.rename("forecast").round(0).clip(lower=0).to_frame()
out.index.name = "month_start"
out.to_csv("forecast_output.csv")
print(out)