Time series data includes timestamps and is often generated while monitoring the industrial process or tracking any business metrics. An ordered sequence of timestamp values at equally spaced intervals is referred to as a time series. Analysis of such a time series is used in many applications such as sales forecasting, utility studies, budget analysis, economic forecasting, inventory studies, and so on. There are a plethora of methods that can be used to model and forecast time series.
In this chapter, we are going to explore Time Series Analysis (TSA) using Python libraries. Time series data is in the form of a sequence of quantitative observations about a system or process and is made at successive points in time.
In this chapter, we are going to cover the following topics:
- Understanding time series datasets
- TSA with Open Power System Data
