Overview

Time Series Analysis Technique is very essentials for Business Forecasting. The MDP aim is to impart knowledge of advanced analytics, particularly in Finance & Economics. Various predictive models will be considered in the workshop, including Moving Average, Regression, Exponential Smoothing and ARIMA, Seasonal ARIMA. These models are highly useful for practitioners and researchers to predict returns and volatility in time series data. The inferences drawn from prediction models will facilitate market researchers, data analysts and managers to evaluate and implement the best alternative strategy.

Objectives

The objective of this program is to provide researchers, academicians, and industry practitioners with an instinctual understanding of time series modelling. This program would emphasize a broad range of techniques for time series data through hands-on-training to the participants in order to equip them with requisite knowledge and skills to draw meaningful insights using R.

Learning Outcomes

Participants will be able to describe and verify mathematical considerations for analyzing time series, including concepts of white noise, stationarity, autocovariance, autocorrelation, verify the properties of linear predictor operator, and apply various linear forecasting techniques,  apply various techniques for the modeling: including parameter estimation, assumption verification, and residual sequence diagnosis, apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models using R.

Pedagogy

Delivering the content would involve a well-balanced combination of virtual classroom lectures, practical hands-on sessions with Excel and R including case base teaching.

Who May Attend?

The program is suitable for managers at various hierarchical levels who want to better understand Time Series Modelling and its impact in their daily operations. The course is addressed to corporates, executives and academicians.

Content of the Programme

• Introduction to Time Series
• Predictive Performance
• Data Partitioning and Overfitting Stationarity, Random Walk, White Noise
• Moving Average,
• Simple Exponential Smoothing,
• Holt’s Smoothing,
• Holt-Winter’s smoothing technique
• ETS models
• Regression Based Forecasting
• Linear, exponential, polynomial trends
• Additive and multiplicative seasonality
• Auto-Correlation
• AR Models Second-layer AR models
• ARIMA Modelling

Programme Directors

Dr. Rinku Jain

Dr. Rinku Jain has over 18 years of research and academic experience. Currently she is working as an Associate Professor in the Business Analytics Department at K J Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai. Prior to SIMSR, she worked as a faculty in the Quantitative department at IBSAR. She completed her Ph.D. and MPhil (Mathematics) from Rajasthan University, Jaipur.  She has conducted MDP and guest lectures on Statistics, Data Analysis, Business Analytics, Big data analytics and Operations Research to various Companies such as Bombay Stock Exchange, National Stock Exchange, and INS HAMLA, Mahindra & Mahindra, NISM etc. She has published several research papers in National and reputed International ABDC category journals and peer reviewed Scopus, IEEE indexed journals, Web of Science. Her research area of interest are Mathematics, Special Functions, statistics, Business Analytics, and Big Data Analytics. She had attended and presented many research papers in National and International Conferences in the US, UK, Malaysia, Dubai, etc.

Dr. Sanjiwani Kumar

Dr. Sanjiwani Kumar has over 19 years of Industry and an academic experience. Currently she is an Associate professor and area chairperson in the Business Analytics Department at K J Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai. She completed her Ph.D. from SNDT University, Postgraduate diploma in Integrated program in Business Analytics from IIM Indore. With her keen interest in Research, she facilitates subjects like Advanced and Basic Business Research, Data Analysis, Business Statistics and Big data analytics. She also delivers a Research methodology course and Dashboarding Using Tableau for PhD Scholars.  Dr. Sanjiwani has published and presented national and international research papers in the research forums, to name a few, International Journal of Applied Finance and Banking, Journal of Medical Marketing and Review of Integrative business and research. Dr Sanjiwani has conducted several Business research modules for INS Hamala and faculty development program at KJ SIM in the year 2013 and 2016 at MIM.  Somaiya Institute of Management. Concurrently she is completing her doctoral studies in the area of Game Theory from NITIE, Mumbai and has completed her post-graduation in Operations Research from University of Delhi.  She joined K J SIM as adjunct faculty in 2019 and prior to that has visiting faculty experience from KJSIM and St. Xavier’s College covering subjects like Descriptive Statistics, Inferential Statistics, Multivariate and Operations Research. With proficiencies in a range of mathematical and statistical tools, her forte lies in logical reasoning and analytical skills. Her teaching and research interests include Quantitative Techniques, Decision Sciences, Operations Research, Statistics, Operations Management and Game Theory. She has also facilitated subjects like Business Research and Financial Analytics.