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time series forecasting

Contributor(s): Matthew Haughn

Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from the geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.

Time series forecasting is performed in a variety of applications including:

  • Weather forecasting
  • Earthquake prediction
  • Astronomy
  • Statistics
  • Mathematical finance
  • Econometrics
  • Pattern recognition
  • Signal processing
  • Control engineering

Time series forecasting is sometimes just the analysis of experts studying a field and offering their predictions. In many modern applications, however, time series forecasting uses computer technologies, including:

Time series forecasting starts with a historical time series. Analysts examine the historical data and check for four patterns of time decomposition, such as trends, seasonal patterns, cyclic patterns and regularity. Many areas within organizations including marketing, finance and sales use some form of time series forecasting to evaluate probable technical costs and consumer demand.

This was last updated in February 2018

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