Time series analysis and forecasting involve working with time-series data, which is a non-static and continuous series of data that varies with time. Extracting useful information from time-series data is challenging, and time-series analysis and forecasting are often used interchangeably. However, they differ in their focus.
Whereas time-series analysis aims to analyze and summarize existing time-series data to study patterns and trends, time-series forecasting aims to predict future trends or values based on old data points or clustering them into groups.
To analyze and forecast time-series data, it is necessary to decompose it into several components or integrants. The five key time series integrants are trend, seasonality, cyclicity, irregularity, and level. The trend component represents the overall direction of the data, while seasonality represents periodic fluctuations in the data. The cyclicity component represents longer-term fluctuations in the data, while irregularity represents random fluctuations that are not predictable. The level component represents the average value of the data over time.
By decomposing time-series data into these components, it is possible to gain insights and make predictions about future trends or values. Let’s dig a bit deeper into them:
The five key integrants of time series analysis are:
