Forecasting: clean a real-world signal, inspect trend and seasonality, then compare classical models against a holdout window.
Speed:ms/stage
Stage: 1 / 5Model: Holt-WintersMAPE: 3.40%
Source Signal
Raw observations with gaps still visible.
source
Jan 2018Jan 2021Dec 2023
Datasets
Six years of monthly sales with trend, seasonality, a few gaps, and anomalies.
Source points: 72 | Missing values: 2
Forecast Model
Walkthrough Focus
Look at the raw timeline first.
Time series work starts with the original signal. We want to spot gaps, spikes, drift, and whether the series feels seasonal before we trust any model.
Forecast Metrics
MAE
324.4
RMSE
453.3
MAPE
3.40%
Signal Controls
Smoothing Parameters
Current Notes
Interpolated 2 missing observations with linear interpolation.
Clipped 4 outliers between the 2th and 98th percentiles.
Holt-Winters additive captures level, trend, and a repeating additive seasonal profile.