1. Understanding the significance of time series analysis in engineering science.
2. Acquaintance with the investigation of stochastic processes and dynamical systems from time series as well as prediction of time series.
3. Understanding the complications in the analysis of real-world time series and how they are addressed, such as non-stationarity and non-normal stochastic process.
4. Acquaintance with computational approaches for the solution of problems in time series analysis.
5. Ability of analyzing real time series in the computer.
Course Content (Syllabus)
Basic characteristics of time series: stationarity; autocorrelation; removal of trends and seasonality; independence test of time series. Linear stochastic processes: autoregressive (AR), moving average (MA), autoregressive moving average (ARMA). Time series models: AR, MA and ARMA for stationary time series; autoregressive integrated moving average (ARIMA) models and seasonal ARIMA (SARIMA) for non-stationary time series. Prediction of time series. Nonlinear analysis of time series: Extensions of linear stochastic models; nonlinear characteristics of time series; nonlinear dynamics and chaos; nonlinear prediction of time series.
Course Bibliography (Eudoxus)
1. Linear Time Series with MATLAB and OCTAVE [electronic resource]
HEAL-Link Springer ebooks, 2019, ISBN: 9783030207908 (Εύδοξος: 91691743)
2. Introduction to Time Series and Forecasting [electronic resource]
Peter J. Brockwell / Richard A. Davis,
HEAL-Link Springer ebooks, Third Edition/2016, (Εύδοξος: 75487888)
3. Introduction to Modern Time Series Analysis [electronic resource]
Gebhard Kirchgassner / Jurgen Wolters / Uwe Hassler
HEAL-Link Springer ebooks, 2nd ed. 2013, ISBN: 9783642334368 (Εύδοξος: 73243237)
Additional bibliography for study
1. The Analysis of Time Series, An Introduction, Chatfield C., Sixth edition, Chapman & Hall, 2004
2. Nonlinear Time Series Analysis, Kantz H. and Schreiber T., Cambridge University Press, 2004
4. Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance, Michael Small, World Scientific