Application of time series modelling for Business forecasting

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The field of applied econometrics with business implications has emerged over the last decade. Keeping this in mind, the present program aims to present the quantitative models to solve business forecasting using applied predictive analytics. Quantifying business problems and solving through applied econometrics using latest industry friendly interface like python make the program challenging. This course is intended to help practitioners cut through the vast literature on econometric models and techniques of predictive analytics. The course is designed for researchers and practitioners in the private and public sector. Our aim is to provide a road map from academic perspective to the research issues that are important for researchers and practitioners.

This short course aims to discuss the broader aspect of econometric modeling and predictive analytics. It aims to cover applied econometric tools relating to univariate and multivariate econometric modeling, Ordered and Multinomial Logistic Regression as well as key aspects of default prediction. The course also aims to discuss the broader aspects of Machine Learning algorithms along with predictive analytics using python.

Business Forecasting using Time Series Data, Seasonality and Univariate Modelling Exploring Cause and Effect: Predictive Analytics Discrete Choice Model: Logistic Regression, Ordered Logistic Regression, Multinomial Logistic Regression Default Probability, Loss Given Default, Expected Loss and Credit Risk Introduction to Machine Learning: Regression Model, Polynomial

Researchers, Academicians and Industry Participants

Course Details

Venue Online
Duration 18 Hrs.
Starts On Sep 23, 2022
Faculty Prof. Ajaya Kumar Panda

Fees Details

Duration Professional Fee*(Per participant) GST(18%) Total Fees(Per Participant) Programme Code
18 Hrs. 9,000.00 1,620.00 10,620.00 1 23 2 24
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