fpp3 Workthrough — Forecasting: Principles and Practice (3rd ed)

Book: https://otexts.com/fpp3/ Goal: Work through chapter by chapter, learning concepts and building tablecloth.time as we go.

R → Clojure Translation Guide

R (fpp3 stack)	Clojure equivalent
tsibble	tech.ml.dataset + tablecloth
tibble / dplyr	tablecloth.api
lubridate	tablecloth.time.column.api + java.time
ggplot2	TBD (Hanami? Vega? Clerk?)
feasts (features)	tablecloth.time (to build)
fable (models)	tablecloth.time or separate lib (to build)
fpp3 (datasets)	Need to port or load equivalent datasets

Progress

Chapter 1: Getting started

• [ ] Read through
• [ ] Notes on key concepts

Chapter 2: Time series graphics

• [ ] 2.1 tsibble objects
• [ ] 2.2 Time plots
• [ ] 2.3 Time series patterns
• [ ] 2.4 Seasonal plots
• [ ] 2.5 Seasonal subseries plots
• [ ] 2.6 Scatterplots
• [ ] 2.7 Lag plots
• [ ] 2.8 Autocorrelation
• [ ] 2.9 White noise

Chapter 3: Time series decomposition

• [ ] 3.1 Transformations and adjustments
• [ ] 3.2 Time series components
• [ ] 3.3 Moving averages
• [ ] 3.4 Classical decomposition
• [ ] 3.5 Methods used by official statistics agencies
• [ ] 3.6 STL decomposition

Chapter 4: Time series features

• [ ] 4.1 Some simple statistics
• [ ] 4.2 ACF features
• [ ] 4.3 STL features
• [ ] 4.4 Other features
• [ ] 4.5 Exploring Australian tourism data

Chapter 5: The forecaster's toolbox

• [ ] 5.1 A tidy forecasting workflow
• [ ] 5.2 Some simple forecasting methods
• [ ] 5.3 Fitted values and residuals
• [ ] 5.4 Residual diagnostics
• [ ] 5.5 Distributional forecasts and prediction intervals
• [ ] 5.6 Forecasting using transformations
• [ ] 5.7 Forecasting with decomposition
• [ ] 5.8 Evaluating point forecast accuracy
• [ ] 5.9 Evaluating distributional forecast accuracy
• [ ] 5.10 Time series cross-validation

Chapter 6: Judgmental forecasts

• [ ] Read through (conceptual, not much to implement)

Chapter 7: Time series regression models

• [ ] 7.1–7.9

Chapter 8: Exponential smoothing

• [ ] 8.1–8.7

Chapter 9: ARIMA models

• [ ] 9.1–9.9

Chapter 10: Dynamic regression models

• [ ] 10.1–10.6

Chapter 11: Hierarchical and grouped time series

• [ ] 11.1–11.6

Chapter 12: Advanced forecasting methods

• [ ] 12.1–12.5