Learn the basics of time series analysis in plain language. This module covers how to look at a time series, check for stationarity, and decompose it into trend, seasonality, and residuals.

Start here:

• Time Series - A Friendly Guide — A beginner-friendly introduction to stationarity, trend, seasonality, and additive vs multiplicative decomposition.
• Time Series Part 2 - Simple Models and Smoothing — Naive, mean, drift, seasonal naive, simple exponential smoothing, Holt’s, Holt-Winters, and how parameters are chosen.
• Time Series Part 3 - Autoregressive Models and Stationarity — AR models, stationarity tests (ADF, KPSS), handling non-stationarity, ACF and PACF.
• Time Series Part 4 - ARMA, ARIMA and SARIMA — Moving Average, ARMA, ARIMA, SARIMA, and a worked example on monthly hot-chocolate sales.
• Time Series Part 5 - Practical Forecasting — White noise, forecast horizons, walk-forward validation, forecast intervals, and deep learning for time series.

What You’ll Learn:

• What makes time-series data different from normal tabular data
• How to check whether a series has a constant mean and variance
• The difference between trend and seasonality
• How to decompose a series additively or multiplicatively
• Why the order of observations matters