Time: 2:30pm-4:00pm CET
Duration: 90 minutes
1st Chapter: The Concept of Time Series
Definition, data type, purpose, plots, trends, and seasonal variation, decomposition of series, models, estimating trends and seasonal effects, smoothing, applications using R.
2nd Chapter: Correlation
Purpose, Expectation and the Ensemble: expected value, the ensemble and stationarity, ergodic series, variance function, autocorrelation, the correlogram: general discussion, and illustrative examples, covariance of sums of random variables, examples in real-life situation.
3rd Chapter: Forecasting Strategies
Purpose, Leading variables and associated variables, Bass models: exponential smoothing and the Holt-Winters method. Application using R language.
4th Chapter: Basic Stochastic Models
Purpose, white noise, random walk, fitted models and diagnostic plots, autoregressive models and applications in real-world using R language.