Prediction is very difficult, especially if it's about the future - Niels Bohr
One day course designed to give an introduction to the practice of time series forecasting.
00
- what determines a good time series forecast
- applications of time series
- what trend, seasonality, stationarity are
- understand the simple baseline approaches
- understand log & difference transforms
01
- missing values
02
- know how to decompose a time series
- know how to check if our time series is a random walk
- understand what autocorrelation is
03
- options for engineering time features
04
- understand the different error metrics options
05, 06
- understand the issues in evaluating the out of sample performance in time series
- use XGBoost for time series modelling
07 - 10
- classical time series with the Holt-Winters method
- classical time series with ARIMA, SARIMA & SARIMAX
11
- exercise applying SARIMA
Forecasting: Principles and Practice - Rob J Hyndman and George Athanasopoulos - textbook