Using Data Mining in Forecasting Problems

In today's ever-changing economic environment, there is ample opportunity to leverage the numerous sources of time series data now readily available to the savvy business decision maker. This time series data can be used for business gain if the data is converted to information and then into knowledge. Data mining processes, methods and technology oriented to transactional-type data (data not having a time series framework) have grown immensely in the last quarter century. There is significant value in the interdisciplinary notion of data mining for forecasting when used to solve time series problems. The intention of this talk is to describe how to get the most value out of the myriad of available time series data by utilizing data mining techniques specifically oriented to data collected over time; methodologies and examples will be presented. Introduction, Value Proposition and Prerequisites Big data means different things to different people. In the context of forecasting, the savvy decision maker needs to find ways to derive value from big data. Data mining for forecasting offers the opportunity to leverage the numerous sources of time series data, internal and external, now readily available to the business decision maker, into actionable strategies that can directly impact profitability. Deciding what to make, when to make it, and for whom is a complex process. Understanding what factors drive demand, and how these factors (e.g. raw materials, logistics, labor, etc.) interact with production processes or demand, and change over time, are keys to deriving value in this