Time Series Forecasting Lab

Create a comprehensive time series forecasting workbench that supports multiple algorithms (moving average, exponential smoothing, ARIMA-style, neural prophet). Users compare model accuracy, tune parameters visually, and the AI explains which model works best and why.

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Objectives

Implement multiple forecasting methods with configurable parameters
Split data into train/test sets and evaluate forecast accuracy (MAE, RMSE, MAPE)
Visualize forecasts with prediction intervals overlaid on actual data
Enable side-by-side model comparison with accuracy rankings
Use AI to recommend the best model and explain the reasoning

Hints

Implement exponential smoothing from scratch — it's just weighted averages
Use cross-validation with expanding windows for more robust accuracy estimates
Show residual plots alongside forecasts so users can spot systematic errors

Resources

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