TIME SERIES ANALYSIS & FORECASTING (STAT-616)

The aim of this course is to impart basic and applied knowledge about time series and its applications in different fields with an emphasis on practical aspects of the interpretation of statistically based conclusions in research. It deals with the method of data collection, description measures of data interpretation of results, model selection, decision making and forecasting. This course is designed for the modelling and forecasting of time series data. This focuses on the decomposition of time series, stationary data, models for stationary and non-stationary series, forecasting methods and properties of models like mean, variance, auto-covariance and auto-correlation function.

Learning Outcomes

To impart basic and applied knowledge about Time Series and its applications in different fields.
To impart skills on the data collection, description measures of data interpretation of results, model selection, decision making and Forecasting.

The recommended books for this study is:

Chatfield, C. (1996). “The Analysis of Time Series: An Introduction”, Chapman and Hall, London.
Brock well P.J. and Davis R.A.(1991).”Time Series Theory and Methods”, Springer Verlag New York.

Week

Topics and Readings

Book

Time Series, Different types of time series, Objective of time series

The Analysis of Time Series (1-7)

Components of time series data, Trend, Secular trend and there difference, Seasonal Variation Cyclical Fluctuation, Irregular Variations

The Analysis of Time Series (13-17)

Decomposition of time Series Data Recomposition of time Series Data

The Analysis of Time Series (17-22)

Exponential smoothing technique, simple exponential smoothing

The Analysis of Time Series (Slides)

Holts corrected trend exponential smoothing, Holt’s winter exponential

The Analysis of Time Series (Slides)

Transformation of Data and its type, Box-Cox transformation Stationary Process, Strict stationary, Weak stationary, Trend stationary, Difference stationary.

The Analysis of Time Series (10-13)

How to Handle real life Data, Missing values, trend analysis, seasonal analysis, Outliers, its detection, techniques and Stochastic Process.

The Analysis of Time Series (22-30)

Autocovariance function, Autocorrelation function, Partial Autocorrelation function, Acf, PACF, Correlogram. Properties of Autocorrelation function

The Analysis of Time Series (30-40)

Periodogram, spectral density functions, comparison with ACF, Linear stationary models.

The Analysis of Time Series (105-121)

Random Process, mean , variance, autocovariance, autocorrelation function. Random Walk model its mean, variance, autocovariance, autocorrelation function.

The Analysis of Time Series (41-46)

Autoregressive models its mean, variance, autocovariance, autocorrelation function.

The Analysis of Time Series (49-53)

Moving Average models its mean, variance, autocovariance, autocorrelation function.

The Analysis of Time Series (53-56)

Mixed models, Autoregressive Moving Average models (ARMA models) its mean, variance, autocovariance, autocorrelation function

The Analysis of Time Series (56-59)

Non-stationary models, General ARIMA notation and models its mean, variance, autocovariance, autocorrelation function.

The Analysis of Time Series (59-62)

Model Selection, Box and Jenkins Methodology, Forecasting

The Analysis of Time Series (66-75)

Practical work of Linear trend,AR, MA, ARIMA, SARIMA, ANN model on differnt Software