Welcome to the Time Series Forecasting course at ITESO. Here you will find all the materials and resources needed for the course.

1 Time Series Forecasting with R

Modern forecasting workflows using tidy data principles with the tidyverts ecosystem.

1.1 What you’ll learn

  • Build tidy time series with tsibble and modern data pipelines
  • Create forecasts with ETS, ARIMA, and regression-based models via fable
  • Engineer time features and diagnose models with feasts)
  • Evaluate with time-aware resampling and communicate uncertainty
  • Scale to multiple series and hierarchies when needed
TipHow to succeed here

Forecasting improves with iteration: start simple, validate honestly, and refine. Keep a tight loop of fit → diagnose → evaluate → communicate.

1.2 Start here

Note

New to the site? Use the links below to jump in.

1.3 Course map

1.3.1 1. Foundations

  • tidy data + time indexes
  • tsibble keys, gaps, and intervals

1.3.2 2. Patterns & features

  • seasonality, trend, decomposition
  • diagnostics and features (feasts)

1.3.3 3. Core forecasting models

  • ETS + ARIMA
  • model comparison and selection

1.3.4 4. Regression workflows

  • time features, events, and covariates
  • forecast reconciliation ideas

1.3.5 5. Evaluation & communication

  • time series CV and backtesting
  • uncertainty, intervals, and narratives

1.3.6 6. Multiple series

  • grouped models
  • hierarchies and reconciliation

1.4 Quick setup check

Run this once after installing packages to confirm your environment.

Code 
library(tidyverse)
library(tsibble)
library(fable)
library(feasts)

sessionInfo()
WarningCommon pitfalls
  • Data leakage: don’t let future information sneak into features.
  • Random CV: use time-aware resampling/backtesting.
  • Overfitting: a more complex model isn’t automatically better.

1.5 Primary toolchain

We’ll use a tidy workflow centered on:

  • tidyverse for data wrangling
  • tsibble for time-aware tibbles
  • fable for modeling and forecasting
  • feasts for decomposition, features, and diagnostics

Recommended reading: Forecasting: Principles and Practice (free online).

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