Time Series Analysis

Paper Code: 
DSTT 801(C)
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.

 

Students will be able to:

Course

Learning outcomes (at course level

Learning and teaching strategies

Assessment Strategies

Paper Code

Paper Title

DSTT 801(C)

Time Series Analysis

CO 120: Define time series components and their uses

 

CO 121: Predict the time series data using different stationary time series data.

 

CO 122: Construct stationary time series models, nonlinear stochastic models and their applications

 

CO 123: Demonstrate their ability to apply statistics in other fields at an appropriate level

 

CO 124: Demonstrate their ability to apply knowledge acquired from their major to real world models.

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: 

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 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 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: 

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: 

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.
References: 

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: 
2023-2024
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