Applied Data Mining for Forecasting Using SAS. Tim Rey
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used for business gain if the data is converted first to information and then to knowledge—knowing what to make when for whom, knowing when resource costs (raw material, logistics, labor, and so on) are changing or what the drivers of demand are and when they will be changing. All this knowledge leads to advantages to the bottom line for the decision maker when times series trends are captured in an appropriate mathematical form. The question becomes how and when to do so. Data mining processes, methods and technology oriented to transactional type data (data that does not have a time series framework) have grown immensely in the last quarter century. Many of the references listed in the bibliography (Fayyad et al. 1996, Cabena et al. 1998, Berry 2000, Pyle 2003, Duling and Thompson 2005, Rey and Kalos 2005, Kurgan and Musilek 2006, Han et al. 2012) speak to the many methods and processes aimed at building prediction models on data that does not have a time series framework. There is significant value in the interdisciplinary notion of data mining for forecasting when used to solve time series problems. The intention of this book is to describe how to get the most value out of the host of available time series data by using data mining techniques specifically oriented to data collected over time. Previous authors have written about various aspects of data mining for time series, but not in a holistic framework: Antunes, Oliveira (2006), Laxman, Sastry (2006), Mitsa (2010), Duling, Lee (2008), and Lee, Schubert (2011).
In this introductory chapter, we help build the case for using data mining for forecasting and using forecasting as a competitive advantage. We cover the explosion of available economic time series data, the basic background on forecasting, and the limitations of classical univariate forecasting (from a business perspective). We also define what a time series database is and what data mining for forecasting is all about, and lastly describe what the advantages of integrating data mining and forecasting actually are.
1.2 Forecasting Capabilities as a Competitive Advantage
Information Technology (IT) Systems for collecting and managing transactional data, such as SAP and others, have opened the door for businesses to understand their detailed historical transaction data for revenue, volume, price, costs and often times even the whole product income statement. Twenty-five years ago IT managers worried about storage limitations and thus would design “out of the system” any useful historical detail for forecasting purposes. With the decline of the cost of storage in recent years, architectural designs have in fact included saving various prorated levels of detail over time so that companies can fully take advantage of this wealth of information. IT infrastructures were initially put in place simply to manage the transactions. Today, these architectures should also accommodate leveraging this history for business gain by looking at it from an advanced analytics view point. Various authors have discussed this framework in detail (Chattratichat et al. 1999, Mundy et al. 2008, Pletcher et al. 2005, Duling et al. 2008).
Large corporations generally have many internal processes and functions that support businesses—all of which can leverage quality forecasts for business gain. This is beyond the typical supply chain need for having the right product at the right time for the right customer in the right amount. Some companies have moved to a lean pull replenishment framework in their supply chains. This lean approach does not preclude the use of high-quality forecasting processes, methods, and technology.
In addition to those who analyze the supply chain, many other organizations in a corporation can use high-quality forecasts. Finance groups generally control the planning process for corporations and deliver the numbers that the company plans against and reports to Wall Street. Strategy groups are always in need for medium- to long-range forecasts for strategic planning. Executive sales and operations planning (ESOP) demand medium-range forecasts for resource and asset planning. Marketing and sales organizations always need short- to medium-range forecasts for planning purposes. New business development (NBD) incorporates medium- to long-range forecasts in the NPV (net present value) process for evaluating new business opportunities. Business managers themselves rely heavily on short- and medium-term forecasts for their own businesses data but also need to know about the market. Since every penny saved goes straight to a company's bottom line, it behooves a company's purchasing organization to develop and support high-quality forecasts for raw material, logistics, materials and supplies, and service costs.
Differentiating a planning process from a forecasting process is important. Companies do in fact need to have a plan to follow. Business leaders do in fact have to be responsible for the plan. But claiming that this plan is in fact a forecast can be disastrous. Plans are what we “feel we can do” while forecasts are mathematical estimates of what is most likely. These are not the same; but both should be maintained. In fact, the accuracy of both should be maintained over a long period of time. When reported to Wall Street, accuracy in the actual forecast is more important than precision. Being closer to the wrong number does not help.
Given that so many groups within an organization have similar forecasting needs, why not move towards a “one number” framework for the whole company? If finance, strategy, marketing and sales, business ESOP, NBD, supply chain and purchasing are not using the same numbers, tremendous waste can result. This waste can take the form of rework or mismanagement if an organization is not totally aligned with the same numbers. Such cross-organizational alignment requires a more centralized approach that can deliver forecasts that are balanced with input from the business and financial planning parts of the corporation. Chase (2009) presents this corporate framework for centralized forecasting in his book called Demand Driven Forecasting.
1.3 The Explosion of Available Time Series Data
Over the last 15 years, there has been an explosion in the amount of time series-based data available to businesses. To name a few, Global Insights, Euromonitor, CMAI, Bloomberg, Nielsen, Moody's Economy.com, Economagic—not to mention government sources such as www.census.gov, www.statistics.gov.uk/statbase, www.statistics.gov.uk/hub/regional-statistics, IQSS database, research.stlouisfed.org, imf.org, stat.wto.org, www2.lib.udel.edu, and sunsite.berkeley.edu. All provide some sort of time series data—that is, data collected over time inclusive of a time stamp. Many of these services are available for a fee, but some are free. Global Insights (www.ihs.com) contains over 30,000,000 time series. It has been the authors' collective experience that this richness of available time series data is not the same worldwide.
This wealth of additional time series information actually changes how a company should approach the time series forecasting problem in that new processes, methods, and technology are necessary to determine which of the potentially thousands of useful time series variables should be considered in the exogenous or multivariate in an X forecasting problem (Rey 2009). Business managers do not have the time to scan and plot all of these series for use in decision making. Statistical inference is a reduction process and data mining techniques used for forecasting can aid in the reduction process.
In order to provide some structure to data concerning various product lines consumed in an economy, there has long been a code structure used to represent an economies market. Various government and private sources provide this data in a time series format. This code structure is called NAICS (North American Industry Classification System) in North America (www.census.gov/naics). Various sources provide historical data in this classification system, but some also produce forecasts (Global Insights). For global product histories, an international system was recently deployed (ICIS—International Code Industry System). This system is at a higher level than the NAICS codes. For reference, there are cross-walk tables between the two (www.naics.com/). Both of these systems, among others, provide potential Y variables for a corporation's market forecasting endeavors. In some cases, depending on the level of detail