TSA goals and applications

When it comes to analyzing time series, depending on the industry and the type of project, different goals can be pursued, from the simplest to the most complex. Likewise, multiple analytical applications can be developed where TSA plays a crucial role. In this section, we will look at the main goals of time series analysis, followed by some examples of real-world applications.

Goals of TSA

In common practice, TSA is directly associated with forecasting, almost as if it were a synonym for this task. Although the objective of predicting the data for a future horizon is probably the most common (and challenging) goal, we should not assume TSA is only that. Often, the purpose of the analysis is to obtain a correct representation of data over time: think of the construction of a tool for data visualization and business intelligence or analyzing the data of a manufacturing process to detect possible anomalies.

Therefore, there are different objectives in the analysis of time series that can be listed in the following four points:

• Exploratory analysis and visualization: This consists of the use of descriptive analytics tools dedicated to the summary of data points with respect to time. Through these analyses, it’s possible to identify the presence of specific temporal dynamics (for example, trends, seasonality, or cycles), detect outliers/gaps in the data, or search for a specific pattern. In business intelligence, it is critical to correctly represent time series within enterprise dashboards in order to provide immediate insights to business users for the decision-making process.
• Causal effect discovery and simulation: In many sectors, often, it is useful to verify how one or more exogenous variables impact a target variable. For example, how advertising investments on different channels (whether digital or not) impact the sales of a company or how some environmental conditions impact the quality of the industrial production of a particular product. These types of problems are very common and, in data analytics, are frequently addressed through the estimation of multiple regression models (adapted to work well with time series data). Once possible causal relationships are identified, it is possible to simulate the outcome of the objective variable as a function of the values assumed by the exogenous variables.
• Anomaly detection and process control (Figure 1.7): We can use TSA to prevent negative events (such as failures, damage, or performance drops):

Figure 1.7 – Anomaly detection using time series

The main idea is to promptly detect an anomaly during the operation of a device or the behavior of a subject, even if the specific anomaly has never been observed before. For many companies, reducing anomalies and improving quality is a key factor for growth and success; for example, reducing fraud in the banking sector or preventing cyber attacks in IT security systems. In manufacturing, process engineers use control charts to monitor the stability of a production process and also a measurement system. Typically, a control chart is obtained by plotting the data points of a time series related to a specific parameter of the manufacturing process (for example, wire pull strength, the concentration of a chemical, oxide thickness, and more) and adding some control limits, which is useful to identify possible process drifts or anomalies.

• Forecasting: This definitely constitutes the main objective of time series analysis and consists of predicting the future values of a time series observed in the past. The forecasting horizon can be short-term or long-term. There are many methods used to obtain the predicted values; we will discuss these aspects in more detail in the Exploring Time series forecasting techniques section.

Domains of applications and use cases

The fields of application of TSA are numerous. Demand Forecasting and Planning is one of the most common applications, as it’s an important process for many companies (especially retailers) to anticipate demand for products throughout the entire supply chain, especially under uncertain conditions. However, from industry to industry, there are many more interesting uses of TSA. Right now, it would be almost impossible to list all applications where the use of TSA plays an important role in creating business solutions and assets; therefore, we will limit ourselves to a few examples that might give you an idea of the heterogeneity of use cases in the field of TSA.

For instance, consider the following list of examples:

• Workforce planning: For a company operating in the logistics and transportation industry, it is crucial to predict the workload so that the right number of staff/couriers are available to handle it properly. In a workforce planning context, correctly forecasting the volume of parcels to be handled can help to effectively allocate effort and resources, which means eventually improving the bottom line for companies with, typically, low-profit margins.
• Forecasting of sales during promotions: E-commerce, supermarkets, and retailers increasingly use promotions, discount periods, and special sales to increase sales volume; however, stock-out problems are often generated, resulting in customer dissatisfaction and extra operative costs. Therefore, it is essential to use forecasting models that integrate the effects of promotions into sales forecasting in order to optimize warehouses and avoid losses, both economic and reputational.
• Insurance claim reserving: For insurance companies, estimating the claims reserve plays an important role in maintaining capital, determining premiums, and being in line with requirements imposed by the policyholder. Therefore, it is necessary to estimate the future number and amount of claims as correctly as possible. In recent years, actuarial practitioners have used several time series-based approaches to obtain reliable forecasts of claims and estimate the degree of uncertainty of the predictions.
• Predictive maintenance: In the context of the Internet of Things, the availability of real-time information generated by sensors mounted on devices and manufacturing equipment enables the development of analytics solutions that can prevent negative events (such as failures, damage, or drops in performance) in order to improve the quality of products or reduce operating costs. Anomaly detection based on TSA is one of the most widely used methods for creating effective predictive maintenance solutions. In Chapter 11, Anomaly Detection – Predicting Failure with No Failure Examples, we will provide a detailed use case in this area.
• Energy load forecasting: In deregulated energy markets, forecasting the consumption and price of electricity is crucial for defining effective bidding strategies to maximize a company’s profits. In this context, TSA is a widely used approach for day-ahead forecasting.

The applications just listed provide insight into how the application of TSA and forecasting techniques form the core of many processes and solutions developed in different industries.