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AKTU Time Series Analysis and Forecasting (BAI076) Syllabus PDF – B.Tech 4th Year
Time Series Analysis and Forecasting BAI076
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Official AKTU syllabus of Time Series Analysis and Forecasting (BAI076) for B.Tech 4th Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Time Series Analysis and Forecasting
Download PDFBAI076 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course , the student will be able to understand
| CO 1 | Analyze any time series data using various statistical approaches. | K2 , K3 |
| CO 2 | Know basic concepts of univariate time series analysis; build appropriate econometric time series | K3, K4 |
models.
| CO 3 | Know basic concepts of multivariate time series analysis; build appropriate econometric time | K1 , K2 |
series models.
| CO 4 | Understand limitation and relevance of the models. | K1 , K2 |
| CO 5 | Generate reasonable forecast values, and to make concise decisions based on forecasts obtained | K2 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | INTRODUCTION OF TIMESERIES ANALYSIS: Introduction to Time Series and Forecasting, Different types of data, Internal structures of time series. Models for time series analysis, Autocorrelation and Partial autocorrelation. Examples of Time series Nature and uses of forecasting, Forecasting Process, Data for forecasting, Resources for forecasting. | 08 |
| II | STATISTICS BACKGROUND FOR FORECASTING: Graphical Displays, Time Series Plots, Plotting Smoothed Data, Numerical Description of Time Series Data, Use of Data Transformations and Adjustments, General Approach to Time Series Modeling and Forecasting, Evaluating and Monitoring Forecasting Model Performance. | 08 |
| III | TIME SERIES REGRESSION MODEL: Introduction Least Squares Estimation in Linear Regression Models, Statistical Inference in Linear Regression, Prediction of New Observations, Model Adequacy Checking, Variable Selection Methods in Regression, Generalized and Weighted Least Squares, Regression Models for General Time Series Data, Exponential Smoothing, First order and Second order. | 08 |
| IV | AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS: Autoregressive Moving Average (ARMA) Models – Stationary and Inevitability of ARMA Models - Checking for Stationary using Variogram- Detecting Non-stationary - Autoregressive Integrated Moving Average (ARIMA) Models - Forecasting using ARIMA - Seasonal Data -Seasonal ARIMA Models Forecasting using Seasonal ARIMA Models Introduction - Finding the “BEST” Model -Example: Internet Users Data Model Selection Criteria - Impulse Response Function to Study the Differences in Models Comparing Impulse Response Functions for Competing Models . | 08 |
| V | MULTIVARIATE TIME SERIES MODELS AND FORECASTING: Multivariate Time Series Models and Forecasting, Multivariate Stationary Process, Vector ARIMA Models, Vector AR (VAR) Models, Neural Networks and Forecasting Spectral Analysis, Bayesian Methods in Forecasting. | 08 |
Text Books
- Introduction To Time Series Analysis And Forecasting, 2nd Edition, Wiley Series In Probability And Statistics, By Douglas C. Montgomery, Cheryl L. Jen(2015)
- Master Time Series Data Processing, Visualization, And Modeling Using Python Dr. Avishek Pal Dr. Pks Prakash (2017)
- Kendall M.G. (1976): Time Series, Charles Griffin.
- Chatfield C. (1980): The Analysis of Time Series –An Introduction, Chapman & Hall.
- Mukhopadhyay P. (2011): Applied Statistics, 2nd ed. Revised reprint, Books and Allied
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