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STATISTICAL FORECASTING TECHNIQUES, DATA MINING AND TREATMENTS
 

12 & 13 July 2018 - M Hotel, Singapore


The Course
 

The workshop is specifically designed to address key issues in the minds of those with management and executive responsibilities and those who work alongside them. The objective of this course it to provide business professionals with a set of data and forecasting procedures and to demonstrate with illustrative examples how such procedures are used in data preparation and model building and forecasting. The course focuses on conceptual framework of models, and thus providing a clear vision of what each model represents, it’s underlying assumptions and what its model statistics imply. Such understanding helps forecasters and business professionals not only in evaluating models but also in selecting the right one for preparing forecasts. The limitations of each of the models are pointed out throughout the course as well. The main goal is to show the application of models in forecasting and the importance of quality of the data used to generate the forecasts. The most important thing for practicing forecasters and their management is to understand the concept, how a model works, how to evaluate it, and how to go about to improve upon it. The requirements of mathematics are kept to a minimum, as most of the forecasting software systems do this on their own.

There are many software packages in the market which have a built in expert system that automatically selects the ‘best’ model (aka ‘best fit’) and then provides the resulting forecasts. However, with the expert system comes the danger of ‘black box’ forecasting. The model selected by software may or may not be the best one. To avoid black box model building, it is important for forecasters to understand what goes behind each model, as well as how and why a given model is chosen. On this account, we will discuss in detail what goes behind each model. We will use real data examples throughout the course to give the live experience from variety of industry sectors. Our goal is to discuss those topics, which have a real application in business and are easy to apply.

This course provides a combination of best practices in data management, segmentation and cleansing approaches required for sound statistical forecasting and forecast presentation … blending years of experience as first-line practitioners and managers with years of management consulting practice as well.

 

How Will I Benefit?

1. Establish a process for effective forecasting.

2. Select the forecasting and analytical techniques most appropriate for any given forecasting problem.

3. Learn how to analyse historical data fast and accurately to improve statistical forecast quality and accuracy.

4. Understand how to evaluate performance of a statistical model.

5. Learn how to interpret ‘residuals’ and model diagnostics to support effective model-building effort.

6. Benefit from understanding techniques of combining various forecasts to improve forecast accuracy and stability.

7. Gain solid understanding of data and the processes generating data and forecasts, thus increasing the ability to manage your staff’s time required to produce more accurate forecasts and decreasing dependence on software logic.

8. Understand the exception driven forecast process and how to implement it in your organization.

9. Learn the ways to incorporate market data into statistical forecasting.

10. Explore various techniques used to transform data to improve the quality of statistical forecast.

11. Understand various trends and how they impact your forecasts.

 

Who Should Attend?

COOs, Directors, VPs, General Managers, Senior Managers, Managers of: Forecasting / Planning, New Product Forecasting, Supply Chain Management, Allocation and Planning, Strategic Planning, Demand Management Process, Brand Management, Promotions Planning, Finance, Production Planning, Merchandising, Product Life-Cycle, Trade Promotions, Retail Collaboration, Sales, Marketing, Market Research, Sales Analysis, Statistical Modelling

 

 

   
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