What is the primary purpose of time series analyses?

The primary purpose of time series analyses is to forecast future trends based on past data. Time series analysis involves collecting data points at successive equally spaced intervals over time. By analyzing trends and patterns within this temporal data, analysts can make informed predictions about future values or behaviors. This predictive capacity is crucial in various fields, including finance, economics, and environmental science, where understanding how variables change over time allows for better decision-making and strategic planning.

The focus is on recognizing trends, seasonal effects, and cyclic patterns that can inform forecasts. Through methods like exponential smoothing, autoregressive integrated moving average (ARIMA), and seasonal decomposition of time series (STL), time series analyses enable users to effectively extract valuable insights from historical data to anticipate future occurrences.