Model guide & examples

Every example on the following pages reuses the same sample series: four years of monthly data with a linear trend and yearly seasonality, generated once and shared across pages so the outputs are directly comparable.

import numpy as np
import pandas as pd

rng = np.random.default_rng(7)
t = np.arange(48)
trend = 100 + 2.2 * t
season = 12 * np.sin(2 * np.pi * t / 12)
noise = rng.normal(0, 2, size=48)

y = pd.Series(
    (trend + season + noise).round(1),
    index=pd.period_range("2022-01", periods=48, freq="M"),
    name="sales",
)

2022-01    100.0
2022-02    108.8
2022-03    114.2
...
2025-10    183.9
2025-11    192.5
2025-12    197.6
Freq: M, Name: sales, Length: 48, dtype: float64

• NaiveForecaster
• SeasonalNaiveForecaster
• MeanForecaster
• DriftForecaster
• ThetaForecaster
• ETSForecaster
• ARIMAForecaster
• AutoARIMAForecaster
• LSTMForecaster
• AutoForecaster

Real-data walkthrough

Every page above uses the synthetic series generated above, chosen for compact, exactly reproducible output. For a longer, end-to-end walkthrough on a real dataset – weekly % of inpatient insurance claims with a flu diagnosis in California, from the CMU Delphi Epidata API – see the notebook below. It covers forecasting with uncertainty bands, rolling-origin backtesting, accuracy-by-horizon comparisons, and AutoForecaster’s model selection and candidate overlay, all with plots.

• Epidemiological Time Series Forecasting & Plotting