Predictive Analytics For Business Forecasting

Overview

The goal of this programme is to provide a thorough understanding of the tools and techniques utilised in forecasting for managerial decision-making. This includes addressing challenges such as demand estimation, market size determination, and sales projections. The methodology will cover a range of time series analysis techniques and regression methods, presented through a combination of case studies and numerical demonstrations. Participants will also have the opportunity to utilise software packages, such as R, to enhance their forecasting capabilities.

Objectives

• Understanding forecasting fundamentals: Participants will gain an understanding of the basic principles, methods and types of business forecasting.
• Data analysis and interpretation: Participants will learn to analyse historical data, identify patterns and make data-driven using statistical tools.
• Forecasting techniques: Explore various quantitative and qualitative forecasting methods, including time series analysis, regression analysis and market research.

Who can attend

The programme is designed for executives with an analytical mindset who are looking to utilise various models for forecasting. The programme will begin with a basic overview of statistical techniques. It is recommended that participants have some familiarity with elementary statistics at the 10+2 or undergraduate level, as well as previous exposure to forecasting problems in their professional work. While hands-on experience in solving such problems is not required, it is beneficial.

Key highlights

Introduction to Business Forecasting

• Qualitative Techniques & Quantitative Techniques
• Time series Forecasting – Components, Decomposition, Smoothening Methods, ARIMA Methods
• Causal Techniques – Linear Regression, Multiple Regression
• Basic Statistical concepts
• Time series forecasting
• Naïve Models, Moving average, Exponential Smoothening, ARIMA.