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We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form yt = f(yt 1; : : : ; yt L), the prediction of y at time t+ k is based on the estimates ŷt+k 1; : : :...

Multi-step-ahead prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation errors. This paper presents a novel approach predicting plant growth agriculture, focusing Stem Diameter Variations (SDV). The proposed consists three ...

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, w...

In recent decades, natural calamities such as drought and flood have caused widespread economic social damage. Climate change rapid urbanization contribute to the occurrence of disasters. addition, their destructive impact has been altered, posing significant challenges efficiency, equity, sustainability water resources allocation management. Uncertainty estimation in hydrology is essential for...

The problem of automatic and accurate forecasting time-series data has always been an interesting challenge for the machine learning community. A majority real-world problems have non-stationary characteristics that make understanding trend seasonality difficult. applicability popular deep neural networks (DNNs) as function approximators TSF is studied. following DNN models are evaluated: Multi...

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...