Time Series Modelling of Water Resources and Environmental Systems
This is a comprehensive presentation of the theory and practice of time series modelling of environmental systems. A variety of time series models are explained and illustrated, including ARMA (autoregressive-moving average), nonstationary, long memory, three families of seasonal, multiple input-single output, intervention and multivariate ARMA models. Other topics in environmetrics covered in this book include time series analysis in decision making, estimating missing observations, simulation, the Hurst phenomenon, forecasting experiments and causality. Professionals working in fields overlapping with environmetrics - such as water resources engineers, environmental scientists, hydrologists, geophysicists, geographers, earth scientists and planners - will find this book a valuable resource. Equally, environmetrics, systems scientists, economists, mechanical engineers, chemical engineers, and management scientists will find the time series methods presented in this book useful.
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LINEAR NONSEASONAL MODELS
FORECASTING AND SIMULATION
LONG MEMORY MODELLING
MULTIPLE INPUT SINGLE OUTPUT MODELS
algorithm annual applications approach appropriate ARMA model autocorrelation autoregressive average monthly Box-Cox transformation calculated CARMA Chapter coefficient confidence limits confirmatory data analysis considered correlation covariate series data set defined deseasonalized detecting diagnostic checks differencing employed environmental equation example exploratory data analysis FARMA Figure flows given graph Hipel homoscedastic Hurst phenomenon identification input series intervention analysis intervention model likelihood function linear logarithmic Mann-Kendall matrix maximum likelihood McLeod mean level methods missing observations MLE's MMSE forecasts model construction model parameters model to fit noise term nonparametric tests nonseasonal nonstationary normally distributed obtain PACF periodic plot procedure quality time series RACF regression analysis residual CCF River sample ACF SARIMA Saugeen Saugeen River seasonal models sequence Series Analysis series models simulation stages of model stationary stochastic models sunspot Table techniques TFN model theoretical ACF tion transfer function transformed series variance Water Resources Research white noise