# Time Series Analysis

## Abstract

Hydroclimatic variables such as rainfall intensity, streamflow, air temperature vary with space and time, due to different hydrological/climatic phenomena/processes. As these processes are continuously evolving over time, studying the interdependence in hydroclimatic data with proper consideration of temporal information may lead to better insight into the governing processes. Observations of any variable, recorded in chronological order, represent a time series. A time series is generally assumed to consist of deterministic components (results can be predicted with certainty) and stochastic components (results cannot be predicted with certainty as the outcome depends on chance). Analysis of time series helps to get an insight of the time series that in turn may enhance the prediction of the hydroclimatic processes/variables. The objective of this chapter is to introduce different types of time series analysis techniques. This requires an understanding of time series analysis techniques and time series properties like stationarity, homogeneity, periodicity, which is the subject matter of this chapter.