∇ Lesson 1
∇ Lesson 2
Introduction to stochastic processes, autocorrelation, and stationarity
∇ Lesson 3
Different plot types can be used to visualize and perform data analysis when working with time series data.
∇ Lesson 4
Stationarity and how to make the future more similar to the past.
∇ Lesson 5
Many time series forecasting techniques can be formulated as State Space models. Here we briefly introduce a single notation that can be used to describe both exponential smoothing families and ARIMAX models.
∇ Lesson 6
How to enforce linear relations in forecasted time series, such that, for example, we produce consistent forecasts for a group of time series and their partial aggregations?
∇ Lesson 7
Introduction to ARIMA models
∇ Lesson 8
How can we generate probabilistic forecasts when Gaussianity and IID assumptions do not hold?
∇ Lesson 9
Cross-validation and multiple comparison tests for model selection with time series
∇ Lesson 10
How can we use our probabilistic forecasts for taking optimal decisions under uncertainty?
