In this study, the performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) models was investigated evaluated using daily confirmed cases COVID-19 in Nigeria. The stationarity status data collected established Augmented Dickey Fuller unit root test. residual normality test also carried out with plots indicating adequacy fitted ARIMA model. results ne...

This study compared the methods used to forecast increases in power consumption caused by rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced various factors, including condition a vehicle, battery’s state-of-charge (SOC), and distance destination. However, su...

In this paper the water quality forecasting at the Nanjinguan water quality monitoring station of Yangtze River, China, is presented. The time series data used are weekly water quality data obtained directly from Nanjinguan station measurements over the course of five years. In order to forecast water quality, hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models a...

The present study emphasizes the forecast of Andhra Pradesh's total marine fish production and catch commercially important fishes, viz., Indian Mackerel, Oil Sardine, Horse Lesser Sardines for next 5 years by different statistical machine learning approaches under climate change scenario. Forecasting is done with without inclusion climatic environmental parameters in models. Exogenous variable...

Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

In statistics, signal processing, and mathematical finance; a time series is a sequence of data points that measured at uniform time intervals. The prediction of time series is a very complicated process. In this paper, an improved Adaptive Neuro Fuzzy Inference System (ANFIS) is taken for predicting Mackey-Glass which is one of the chaotic time series. In the modeling of linear and stationary ...

In recent decades artificial neural networks (ANNs) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. This paper presents the application of artificial neural networks to predict drought in Yazd meteorological station. In this research, different archite...