Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain va...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

The massive spread of COVID-19 has disrupted trading activities worldwide plunging the economy a nation; particularly, trade-dependent nations are severely affected by restriction in exports and imports. This paper aims to evaluate implications on trade New Zealand exploratory data analysis ARIMA modeling. Based comprehensive strategy prediction, were processed notice impact pandemic sales. (Au...

Water resources are an indispensable and valuable resource for human survival development. quality predicting plays important role in the protection development of water resources. It is difficult to predict due its random trend changes. Therefore, a method which combines Auto Regressive Integrated Moving Average (ARIMA) clustering model was proposed this paper. By taking monitoring data certai...

Understanding the influence of meteorological parameters in relation to COVID-19 transmission may be a convenient way predict ongoing pandemic towards its adaptive control measures. This study aims explore association between cases and for an extended period covering different climatic patterns. The number cases, daily records rainfall, temperature, relative humidity wind speed data were collec...

in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variat...

Today, there is a crucial need for precise monitoring and prediction of energy consumption at the building level using latest technologies including Internet Things (IoT) data analytics to determine enhance usage. Data-driven models could be used prediction. However, due high non-linearity between inputs outputs models, these improvement in terms accuracy robustness. Therefore, this work aims p...

Overnight forecasting is a crucial challenge for revenue managers because of the uncertainty associated between demand and supply. However, there limited research that focuses on predicting daily hotel demand. Hence, this paper evaluates various models’ traditional time series performances at multiple horizons. The models include seasonal naïve, Holt–Winters (HW) triple exponential smoothing, a...