The time series analysis is used in statistics as a method where data is collected over a specific period of time to identify patterns, trends, and predictions from it.
Methodically, time series analyses can be classified as follows:
Trend analysis: Examining changes over time to identify long-term trends.
Seasonal analysis: Examining recurring patterns in the data.
Cycle analysis: Analysis of recurring patterns that can last longer than a year.
Time series analysis with exogenous variables: Exploring relationships between the time series and other variables that are not included in the time series.
One of the most common applications of time series analysis is the prediction of future events or trends.
Example
An online shop is examining the sales figures of its products over a period of several years. The analysis can reveal whether there are seasonal patterns in the sales, whether there is a long-term trend, or whether certain events like discount promotions have an impact on the sales.
Resources:
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2nd ed.). John Wiley & Sons.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). John Wiley & Sons.