Applied Data Mining for Forecasting Using SAS. Tim Rey
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even be considered Xs.
Many of these sources offer databases for historical time series data but do not offer forecasts themselves. Other services, such as Global Insights and CMAI, do in fact offer forecasts. In both of these cases though, the forecasts are developed based on an econometric engine versus simply supplying individual forecasts. There are many advantages to having these forecasts and leveraging them for business gain. How to do so by leveraging both data mining and forecasting techniques will be discussed in the remainder of this book.
1.4 Some Background on Forecasting
A couple of important distinctions about time series models are important at this point. First, the one thing that differentiates time series data from transaction data is that the time series data contains a time stamp (day, month, year.) Second, time series data is actually related to “itself” over time. This is called serial correlation. If simple regression or correlation techniques are used to try and relate one time series variable to another, without regard to serial correlation, the business person can be misled. Therefore, rigorous statistical handling of this serial correlation is important. Third, there are two main classes of statistical forecasting approaches detailed in this book. First there are univariate forecasting approaches. In this case, only the variable to be forecast (the Y or dependent variable) is considered in the modeling exercise. Historical trends, cycles, and the seasonality of the Y itself are the only structures considered when building the univariate forecast model. In the second approach, where a multitude of time series data sources as well as the use of data mining techniques come in, various Xs or independent (exogenous) variables are used to help forecast the Y or dependent variable of interest. This approach is considered multivariate in the X or exogenous variable forecast model building. Building models for forecasting is all about finding mathematical relationships between Ys and Xs. Data mining techniques for forecasting become all but mandatory when 100s or even 1000s of Xs are considered in a particular forecasting problem.
For reference purposes, short-range forecasts are defined as one to three years, medium-range forecasts are defined as three to five years, and long-term forecasts are defined as greater than five years. Generally, the authors agree that anything greater than 10 years should be considered a scenario rather than a forecast. More often than not, in business modeling, quarterly forecasts are being developed. Quarterly data is the frequency that the historical data is stored and forecast by the vast majority of external data service providers. High-frequency forecasting might also be of interest even in finance where data can be collected by the hour or minute.
1.5 The Limitations of Classical Univariate Forecasting
Thanks to new transaction system software, businesses are experiencing a new richness of internal data, but, as detailed above, they can also purchase services to gain access to other databases that reside outside the company. As mentioned earlier, when building forecasts using internal transaction Y data only, the forecasting problem is generally called a univariate forecasting model. Essentially, the transaction data history is used to define what was experienced in the past in the form of trends, cycles, and seasonality to then forecast the future. Though these forecasts are often very useful and can be quite accurate in the short run, there are two things that they cannot do as well as the multivariate in X forecasts: They cannot provide any information about the “drivers” of the forecasts. Business managers always want to know what variables drive the series they are trying to forecast. Univariate forecasts do not even consider these drivers. Secondly, when using these drivers, the multivariate in X or exogenous models can often forecast further in time, with accuracy, then the univariate forecasting models.
The 2008–09 economic recession was evidence of a situation where the use of proper Xs in a multivariate in X “leading indicator” framework would have given some companies more warning of the dilemma ahead. Services like ECRI (Economic Cycle Research Institute) provided reasonable warning of the downturn some three to nine months ahead of time. Univariate forecasts were not able to capture these phenomena as well as multivariate in X forecasts.
The external databases introduced above not only offer the Ys that businesses are trying to model (like that in NAICS or ISIC databases), but also provide potential Xs (hypothesized drivers) for the multivariate in X forecasting problem. Ellis (2005) in “Ahead of the Curve” does a nice job of laying out the structure to use for determining what X variables to consider in a multivariate in X forecasting problem. Ellis provides a thought process that, when complemented with the data mining for forecasting process proposed herein, will help the business forecaster do a better job of both identifying key drivers and building useful forecasting models.
Forecasting is needed not only to predict accurate values for price, demand, costs, and so on, but it is also needed to predict when changes in economic activity will occur. Achuthan and Banerji—in their Beating the Business Cycle (2004) and Banerji in his complementary paper in 1999—present a compelling approach for determining which potential Xs to consider as leading indicators in forecasting models. Evans et al. (2002), as well as www.nber.org and www.conference-board.org, have developed frameworks for indicating large turns in economic activity for large regional economies as well as for specific industries. In doing so, they have identified key drivers as well. In the end, much of this work shows that, if we study them over a long enough time frame, we can see that many of the structural relations between Ys and Xs do not actually change. This fact offers solace to the business decision maker and forecaster willing to learn how to use data mining techniques for forecasting in order to mine the time series relationships in the data.
1.6 What is a Time Series Database?
Many large companies have decided to include external data, such as that found in Global Insights, as part of their overall data architecture. Small internal computer systems are built to automatically move data from the external source to an internal database. This practice, accompanied with tools like the SAS® Data Surveyor for SAP (which is used to extract internal transaction data from SAP), enables both the external Y and X data to be brought alongside the internal Y and X data. Often the internal Y data is still in transactional form that, once properly processed, can be converted to time series type data. With the proper time stamps in the data sets, technology such as Oracle, Sequel, Microsoft Access or SAS itself can be used to build a time series database from this internal transactional data and the external time series data. This database would now have the proper time stamp and Y and X data all in one place. This time series database is now the starting point for the data mining for forecasting multivariate in X modeling process.
1.7 What is Data Mining for Forecasting?
Various authors have defined the difference between “data mining” and classical statistical inference (Hand 1998, Glymour et al. 1997, and Kantardzic 2011, among others). In a classical statistical framework, the scientific method (Cohen 1934) drives the approach. First, there is a particular research objective sought after. These objectives are often driven by first principles or the physics of the problem. This objective is then specified in the form of a hypothesis; from there a particular statistical “model” is proposed, which then is reflected in a particular experimental design. These experimental designs make the ensuing analysis much easier in that the Xs are orthogonal to one another, which leads to a perfect separation of the effects therein. So the data is then collected, the model is fit and all previously specified hypotheses are tested using specific statistical approaches. In this way, very clean and specific cause-and-effect models can be built.
In contrast, in many business settings a set of “data” often contains many Ys and Xs, but there was no particular modeling objective or hypothesis in mind when the data was being collected in the first place. This lack of an original objective often leads to the data having multi-collinearity—that is, the Xs are actually related to one another. This makes building cause-and-effect models much more difficult. Data mining practitioners will mine this type of data in the sense that various statistical and machine learning methods are applied to the data looking for specific Xs that might predict the Y with a certain level of accuracy. Data mining on transactional data is then the process of determining what set of Xs best predicts the Ys.