Time Series Analysis

Time series analysisTime series analysis of hydrologic data helps understand the behaviour of a hydrologic variable over time. It aids in identifying long-term patterns and trends in the data, which may enhance the prediction of future water availability and flood or droughtDrought risk. The chapter introduces different types of time series data and discusses the decompositionDecomposition of a time series into its constituent components like trend, cycleCycle, seasonalitySeasonality, and irregularity. Methods to test the stationarityStationarity of a time series, e.g., the Autocorrelation function (ACFAutocorrelation function (ACF)) plot and Augmented Dickey-Fuller (ADF) testAugmented Dickey-Fuller (ADF) test, and approaches to converting data into stationary are presented. Similarly, methods to analyse trend, seasonalitySeasonality and periodicityPeriodicity, e.g., the Mann–Kendall Test, Sen's Slope Estimator, and partial autocorrelation functionPartial autocorrelation function (PACF) plots, are included. The chapter also introduces the time series models like AutoregressiveAutoregressive (AR), Moving AverageMoving averages (MA), AutoregressiveAutoregressive Moving AverageMoving averages (ARMA) and AutoregressiveAutoregressive Integrated Moving AverageMoving averages (ARIMA) models, which are typically used to model stochastic components of a time series.