In This Chapter
Understanding the properties of a time series
Forecasting a time series with decomposition methods and smoothing techniques
Understanding how a time series may be modeled and forecasted with regression analysis
A time series is a sequence of values observed over a period of time. For example, the daily closing price of IBM stock over the past 30 trading days is a time series. The monthly rainfall in Seattle over the past five years is also a time series, as is the daily price of gold over the past two years.
For many applications, a time series is the most useful organization of data. For example, the properties of a stock could be best analyzed with a time series of historical prices. As another example, you might produce a forecast of the demand for a company’s products, based on the past buying patterns of the company’s customers.
This chapter covers the key statistical features needed to analyze a time series, along with three forecasting techniques: decomposition methods, smoothing techniques, and time series regression.
The properties of a time series may be modeled ...