Time Series Forecasting Techniques in Python
summary
A practical overview that claims to teach basic model selection and shows runnable examples: imports from statsmodels, a random toy dataset, fitting AR/MA/ARIMA/SARIMA variants and a SARIMAX with exogenous regressors, plus demos of Prophet and Darts. Notes which methods are univariate versus multivariate (VARMA for vectors), that models use a Kalman filter internally, and that no performance benchmarks or real-world accuracy numbers are provided.
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chapters (19)
- 0:00Introduction
- 1:20Welcome
- 1:38Agenda
- 2:05What is Time Series
- 3:25Applications
- 5:20Time Series Patterns
- 9:03Basic Steps in Forecasting
- 11:15Time Series Methods
- 12:25Auto Regression
- 14:18Moving Average
- 15:31AutoRegressive
- 18:01Cerimo Model
- 19:22Vector Auto Regression
- 20:40holtwinsters exponential smoothing
- 21:34as profit
- 22:47darts
- 23:40darts capabilities
- 24:54Summary
- 26:05Audience Questions
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