Week 1: Introduction to time series with examples, stationarity, non-stationarity and related concepts
Week 2: Time series decomposition and introduction to basic time series models such as Random walk, White noise, AR, MA, ARMA etc. Introducing ACF and PACF plots and model identification
Week 3: Tests for stationarity, expanding non-stationarity and related models such as ARIMA, SARIMA etc. Introduction to differencing and backshift operators
Week 4: Model identification, estimation and diagnostic checking using tests such as Augmented Dickey Fuller, Ljung and Box, etc.
Week 5: Time series forecasting methods such as ARIMA, SMA smoothing, EMA smoothing, Holt Winter’s technique etc. Comparing forecasts using different metrics
Week 6: Introducing fractionally integrated processes such as ARFIMA, long memory property of ARFIMA processes, estimation of parameters etc.
Week 7: Multivariate time series processes such as VAR, VARMA, moments, cross moments and stationarity, Wald representation
Week 8: Error correction models, cointegration for multivariate time series, causality analysis and causality tests, direct Granger procedure, Haugh-Pierce test, Hsiao procedure
Week 9: Fourier transformation, processes in frequency domain, spectral representation of time series, spectral density
Week 10: Introduction to stochastic volatility models such as ARCH, GARCH and their extensions.
Week 11: Introduction to non-linear time series models such as Threshold Autoregressive (TAR), Smooth Transition Autoregressive (STAR), Markov switching models etc.
Week 12: Introduction to machine learning models for time series. Anomaly detection, LSTM, Neural networks in time series
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