Data Mining for Business Analytics

CHAPTER 18Smoothing Methods

In this chapter, we describe a set of popular and flexible methods for forecasting time series that rely on smoothing. Smoothing is based on averaging over multiple periods in order to reduce the noise. We start with two simple smoothers, the moving average and simple exponential smoother, which are suitable for forecasting series that contain no trend or seasonality. In both cases, forecasts are averages of previous values of the series (the length of the series history considered and the weights used in the averaging differ between the methods). We also show how a moving average can be used, with a slight adaptation, for data visualization. We then proceed to describe smoothing methods suitable for forecasting series with a trend and/or seasonality. Smoothing methods are data-driven, and are able to adapt to changes in the series over time. Although highly automated, the user must specify smoothing constants that determine how fast the method adapts to new data. We discuss the choice of such constants, and their meaning. The different methods are illustrated using the Amtrak ridership series.

Python

In this chapter, we will use numpy and pandas for data handling and matplotlib for visualization. Models are built using statsmodels. We also make use of functions singleGraphLayout() and graphLayout() defined in Table 17.2. Use the following import statements to run the Python code in this chapter.

 import required functionality for this chapter