Time Series Analysis

Paper Code: 
24DSTT803
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

The objective of time series analysis is to analyze and model patterns, trends, and relationships within time-dependent data to make accurate predictions, forecasts, and insights for decision-making.

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

24DSTT803

Time Series Analysis

(Theory)

CO 128: Identify and estimate time series components and outline their applications.

CO 129: Analyze, estimate and eliminate the components of a time series.

CO 130: Predict the time series data using different stationary time series data.

CO 131: Examine appropriate models based on data characteristics and apply them to fit time series data accurately.

CO 132: Demonstrate their ability to apply knowledge related to random component methods and forecasting methods to real world models.

CO 133: Contribute effectively in course-specific interaction.

Approach in teaching:

Interactive Lectures,

Group Discussion,

Classroom Assignment

Problem Solving Sessions

 

Learning activities for the students:

Assignments

Seminar

Presentation

Subject based Activities

Classroom Quiz

Assignments

Class Test

Individual Presentation

 

12.00
Unit I: 
Time Series and Trend

Introduction to times series data, application of time series from various fields, Components of a times series, Decomposition of time series. Trend: Estimation of trend by free hand curve method, method of semi averages, fitting a various mathematical curve, and growth curves.  

 

12.00
Unit II: 
Trend and Seasonal Fluctuations

Trend Cont.: Method of moving averages. Detrending. Effect of elimination of trend on other components of the time series. Seasonal Component: Estimation of seasonal component by Method of simple averages, Ratio to Trend. 

12.00
Unit III: 
Seasonal and Cyclic Fluctuations

Seasonal Component cont: Ratio to Moving Averages and Link Relative method, Deseasonalization. Cyclic Component: Harmonic Analysis. Stationary Time series: Weak stationarity, autocorrelation function and the correlogram.

 

12.00
Unit IV: 
Foundations of Time Series Modeling

Some Special Processes: Moving-average (MA) process and Autoregressive (AR) processes. Estimation of the parameters of AR (1) and AR (2). Autocorrelation functions of AR (1) and AR (2) processes. Introduction to ARMA and ARIMA models. Box-Jenkins method. 

 

12.00
Unit V: 
Advanced Forecasting Techniques

Random Component: Variate component method. Forecasting: Exponential smoothing methods, Short term forecasting methods: Brown’s discounted regression. Introduction to ARCH and GARCH models.

 

 

Essential Readings: 
  • Box, G.E.P. and Jenkins, G.M. (1976): Time series analysis—Forecasting and Control, Holden-day, San Francisco.
  • Anderson, T.W. (1971): The Statistical Analysis of Time Series, Wiley, N.Y.
  • Montgemory, D.C. and Johnson, L.A. (1977): Forecasting and Time Series Analysis, McGraw Hill.
  • Kendall, Sir Maurice and Ord, J.K. (1990): Time Series (Third Edition), Edward Arnold.

 

SUGGESTED READINGS:

  • Brockwell, P.J. and Davis, R.A.: Time Series: Theory and Methods (Second Edition), Springer- Verlag.
  • Fuller, W.A. (1976): Introduction to Statistical Time Series, John Wiley, N.Y.
  • Granger, C.W.J. and Newbold (1984): Forecasting Econometric Time Series, Third Edition, Academic Press.
  • Priestley, M.B. (1981): Spectral Analysis & Time Series, Griffin, London.
  • Kendall, M.G. and Stuart A. (1966): The Advanced Theory of Statistics, Volume 3, Charles Griffin, London.
  • Bloomfield, P. (1976): Fourier Analysis of Time Series—An Introduction, Wiley.
  • Granger, C.W.J. and Hatanka, M. (1964): Spectral Analysis of Economic Time Series, Princeton Univ. Press, N.J.
  • Koopmans, L.H. (1974). The spectral Analysis of Time Series, Academic Press.
  • Nelson, C.R. (1973): Applied Time Series for Managerial Forecasting, Holden-Day.
  • Findley, D.F. (Ed.) (1981): Applied Time Series Analysis II, Academic Press.

 

e-RESOURCES:

 

JOURNALS:

  • Sankhya The Indian Journal of Statistics, Indian Statistical Institute
  • Aligarh Journal of Statistics, Department of Statistics and Operations Research, Aligarh Muslim University
  • Afrika Statistika, Saint-Louis Senega University
  • International Journal of Statistics and Reliability Engineering, Indian Association for Reliability and Statistic
  • Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
  • Journal of the Indian Statistical Association, Indian Statistical Association
  • Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna
  • Statistics and Applications, Society of Statistics, Computer and Applications
  • Stochastic Modeling and Applications, MUK Publications and Distributions

 

Academic Year: 
2024-2025
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