Humans and companies collect data all the time, smartphones, app logs, IoT and IIoT implementations, and others collect data mechanisms. Are collected different data kinds and formats. Data collected over regular intervals of time is termed time series data. This data, collected for specific purposes are used to understand patterns and derive insights for one or more use cases. Time series analysis is a technical domain with an extensive choice of techniques that need to be carefully selected depending on the business problem to solve and the nature of the time series data. Collecting data is one of the challenges; real-world data presents complexities that amplify many of the problems in time series analysis, such as missing data, multiple time series, time series length and period overlap, sparse data, volatility, multiple seasonality, effect of hierarchy, and others.

We live in a world where all processes generated by nature are random. It means that the behavior of any natural process presents a random (or stochastic) process and that probabilistic laws control its behavior (Privalsky, 2023). I think that time series analysis can be applied to Software Architecture concerning security, performance, and monitoring. It would include in the software development process control points and log generation such as Non-Functional Requirements, which could be implemented as a specific software control layer, especially architectural aspects, even considering the tools and applications dedicated to monitoring software applications. Let’s go research!