More – Time Series Forecasting

Optional materials to support the course.

These materials are optional and are not required to pass the course.

They are meant to help you:

Refresh prerequisite concepts (statistics, probability, regression)
Work more comfortably in R and the tidyverse ecosystem
Improve your workflow and reproducibility (projects, Quarto, organization)

You are encouraged to use them as needed.

Correlation, dependence, and time series pitfalls

Stat refreshers

A refresher on correlation and statistical dependence, with an emphasis on why correlation can be misleading in time series data.

Pablo Benavides-Herrera

3 min

Expectation, variance, and covariance: measuring uncertainty

Stat refreshers

A refresher on expectation, variance, and covariance, focused on how uncertainty and dependence are quantified in time series.

Pablo Benavides-Herrera

3 min

Pipes and the tidyverse mental model

How to think in tidyverse pipelines: understanding pipes, data flow, and readable transformations in R.

Pablo Benavides-Herrera

4 min

R for Python Users

A conceptual introduction to R for students coming from a Python background, focused on mental models, data workflows, and tidyverse-style thinking.

Pablo Benavides-Herrera

4 min

Random variables: uncertainty as an object

Stat refreshers

A mathematically grounded refresher on random variables and distributions, focused on how uncertainty is represented and used in time series forecasting.

Pablo Benavides-Herrera

3 min

tsibble: thinking in keys and time

An introduction to tsibble and the core ideas of time, keys, and structure in tidyverts-based time series workflows.

Pablo Benavides-Herrera

4 min

Categories

Correlation, dependence, and time series pitfalls

Expectation, variance, and covariance: measuring uncertainty

Pipes and the tidyverse mental model

R for Python Users

Random variables: uncertainty as an object

tsibble: thinking in keys and time