Understanding TSA
When analyzing business data, it’s quite common to focus on what happened at a particular point in time: sales figures at the end of the month, customer characteristics at the end of the year, conversion results at the end of a marketing campaign, and more. Even in the development of the most sophisticated ML models, in most cases, we collect information that refers to different objects at a specific instant in time (or by taking a few snapshots of historical data). This approach, which is absolutely valid and correct for many applications, not only in business, uses cross-sectional data as the basis for analytics: data collected by observing many subjects (such as individuals, companies, shops, countries, equipment, and more) at one point or period of time.
Although the fact of not considering the temporal factor in the analysis is widespread and rooted in common practice, there are several situations where the analysis of the temporal evolution of a phenomenon provides more complete and interesting results. In fact, it’s only through the analysis of the temporal dynamics of the data that it is possible to identify the presence of some peculiar characteristics of the phenomenon we are analyzing, be it sales/consumption data, rather than a physical parameter or a macroeconomic index. These characteristics that act over time, such as trends, periodic fluctuations, level changes, anomalous observations, turning points, and more can have an effect in the short or long term, and often, it is important to be able to measure them precisely. Furthermore, it is only by analyzing data over time that it is possible to provide a reliable quantitative estimate of what might occur in the future (whether immediate or not). Since economic conditions are constantly changing over time, data analysts must be able to assess and predict the effects of these changes in order to suggest the most appropriate actions to take for the future.
For these reasons, TSA can be a very useful tool in the hands of business analysts and data scientists when it comes to both describing the patterns of a phenomenon along the time axis and providing a reliable forecast for it. Through the use of the right tools, TSA can significantly expand the understanding of any variable of interest (typically numerical) such as sales, financial KPIs, logistic metrics, sensors’ measurements, and more. More accurate and less biased forecasts that have been obtained through quantitative TSA can be one of the most effective drivers of performance in many fields and industries.
In the next sections of this chapter, we will provide definitions, examples, and some additional elements to gain a further understanding of how to recognize some key features of time series and how to approach their analyses in a structured way.
