Forecasting: Principles And Practice Apr 2026

Forecasts are equal to the value of the last observation.

Use STL decomposition (Seasonal-Trend decomposition using LOESS) to break down the user's data into Trend, Seasonality, and Remainder components.

Forecasts are equal to the last observed value from the same season. Forecasting: Principles and Practice

A variation of the naive method that allows forecasts to increase or decrease over time based on the average change in historical data. Core Functionality

This interactive tool would let users upload a dataset and instantly compare its performance across the four key benchmark methods mentioned in the "Forecaster's Toolbox" (Chapter 5): Forecasts are equal to the value of the last observation

Display a leaderboard using the book's recommended error metrics like MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) to identify which benchmark is hardest to beat.

Forecasts are equal to the mean of historical data. A variation of the naive method that allows

Include interactive plots that show how parameters like the "smoothing rate" in Exponential Smoothing change the forecast line in real-time. Implementation Resources You can build this using the following tools and libraries: Forecasting: Principles and Practice (3rd ed) - OTexts