In this paper, a hybrid model which combines Extended Kalman Filter (EKF) and Genetic Programming (GP) for forecasting of water demand in Tehran is developed. The initial goal of the current work is forecasting monthly water demand using GP for achieving an explicit optimum formula. In the proposed model, the EKF is applied to infer latent variables in order to make a forecasting based on GP re...

Demand Forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In ...

introduction: accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. water has a special place among the basic human needs, because it not hampers human life. the importance of the i...

This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time series and econometric models, a number of new techniques have emerged in th...

Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, t...

The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singular multivariate beta distributions allowing a fully conjugate Bayesian infere...

Forecasting electricity consumption is one of the most important operational issues in order to the use facility systems and power sources optimally. Electricity demand forecasting process will ultimately have an important role in the economic and security of the energy operating system. The objectives of this research are to forecast long-term electricity demand for 2011-2022 and to provide ma...

We forecast high resolution solar irradiance time series using an exponential smoothing state space (ESSS) model. To stationarize the irradiance data before applying linear time series models, we propose a novel Fourier trend model and compare the performance with other popular trend models using residual analysis and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test. Using the opt...

Bayesian Forecasting encompasses statistical theory and methods in time series analysis and time series forecasting, particularly approaches using dynamic and state space models, though the underlying concepts and theoretical foundation relate to probability modelling and inference more generally. This entry focuses speciically in the time series and dynamic modelling domain, with mention of re...