The aim of this study is to develop the best forecast model using hybrid Gaussian-Nonlinear Autoregressive Neural Network water level with multiple hoursahead for Melaka River. developmentof flood models crucial and has led risk control, policy recommendations, a reduction in human life loss, flood-related property destruction. In research, Artificial (ANN) approach was used by modeling forecas...

Under minimal assumptions, it is established that the sample second moments of the errors of out-of-sample (real time) forecasts of possibly incorrect regARIMA models have asymptotic limits with useful frequency domain formulas. Both OLS and GLS estimates of the mean function are considered. With misspecified regressors, under additional assumptions that do not appear to exclude any regressors ...

As the old saying goes, “Don’t put all of your eggs in one basket lest you drop the basket and lose all of your eggs.” Suppose the head of a forecasting division of a company has two sources of forecasts for the company’s sales, one source being the forecasts generated by the division’s econometrics group using an econometric time series model and the other source being the aggregated forecasts...

In the exponential smoothing approach to forecasting, restrictions are often imposed on the smoothing parameters which ensure that certain components are exponentially weighted averages. In this paper, a new general restriction is derived on the basis that the one-step ahead prediction error can be decomposed into permanent and transient components. It is found that this general restriction red...

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and ...

In practice, signal extraction is based on finite samples X1, ..., XT and, very often, current estimates of the interesting components (t = T ) import: a socalled ‘concurrent’ or ‘real-time’ estimate of the trend or of its turning-points has a strong prospective content, since the future evolution of the time series is likely to be conditioned by this component. Whereas forecasting tools genera...

In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model speci…cations to capture both seasonalities. We investigate whether combining forecas...

In this article, a new kind of stationary zero-inflated Pegram’s operator based integer-valued time series process of order p with Poisson marginal or ZIPPAR(p) is constructed for modelling a count time series consisting a large number of zeros compared to standard Poisson time series processes. Estimates of the model parameters are studied using three methods, namely Yule-Walker, conditional l...