Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

CHAPTER 8 Time Series Data Mining

Times series data mining is an emerging field that holds great opport unities for conversion of data into information. It is intuitively obvious to us that the world is filled with time series data—actually transactional data—such as point-of-sales (POS) data, financial (stock market) data, and Web site data. Transactional data is time-stamped data collected over time at no particular frequency. Time series data, however, is time-stamped data collected over time at a particular frequency. Some examples of time series data are: sales per month, trades per weekday, or Web site visits per hour. Businesses often want to analyze and understand transactional or time series data. Another common activity also includes building models for forecasting future behavior.

Unlike other data discussed in this book, time series data sets have a time dimension that must be considered. While so-called cross-sectional data, which can be used for building predictive models, typically features data created by or about humans, it is not unusual to find that time series data is created by devices or machines, such as sensors, in addition to human-generated data. The questions that arise from dealing with vast amounts of time series are typically different from what have been discussed earlier. Some of these questions will be discussed in this chapter.

It is important to note that time series data is a large component to the big data now being generated. There is a ...