Review of techniques used to identify and analyse patterns in time series data, seasonality and trends, using smoothing and curve fitting techniques and autocorrelations, Introduction of a general class of models commonly used to represent time series data and generate predictions (autoregressive and moving average models). Commonly used modeling and forecasting techniques based on linear regression. Analysis of spatial patterns.
Course Content (Syllabus)
Time series analysis-Introduction-definitions. Identifying patterns in time series data. Stationarity and random noise.Trends, seasonality and their analysis. Linear and non-linear regression. ARIMA (Box and Jenkins), autocorrelation, autoregression. Spectral analysis (single-spectrum-Fourier, cross-spectrum, coherence). Spatial pattern analysis (Principal Component Analysis and EOF)
Additional bibliography for study
1. H. von Storch and F. W. Zwiers, Statistical Analysis in Climate Research, Cambridge University Press, 1999.
2. Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis, Forecasting and Control, 3rd ed. Prentice Hall, Englewood Clifs, NJ.