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Time series analysis

Outline: Time Series in Practice

  • When and why do we need time series models?
  • Basic models and definitions: white noise, AR1, MA, random walk, stationarity.
  • 3 approaches to time series modelling: ARIMA, Regression, Structural time series / state-space models

understand basic difficulties with time series, construct a few simple but useful models

References

More technical:

Motivating Example: Mauna Loa Atmospheric CO2 Concentration

Shark Attacks in Florida

Source: R package bsts (Bayesian structural time series)

Financial

fpp2 is the package for the book by Hyndman: Forecasting: Principles and Practice, 2nd edition.

Sunspot Area

Electricity Production

Treerings