The standardized precipitation index (SPI) was used to quantify the classification of drought in the Guanzhong Plain, China. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecast the SPI series. Most of the selected ARIMA models are seasonal models (SARIMA). The forecast results show that the forecasting power of the ARIMA models increases with the incre...

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In par...

Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMA...

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...

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With increase installed WWTPs worldwide, modeling and forecast their energy have become a critical factor in WWTP design meet environmental economic requirements. The accurate swift forecasting soft-sensors are not only supportive daily electric financial budgeting by practitioners on micro-scale, but also b...

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...

Abstract Auto-regressive integrated moving average (ARIMA) assessment of traffic noise was conducted on different routes in Port Harcourt, Nigerian metropolis. This achieved by measuring the various asphalt flexible and concrete rigid pavement structures with a meter for sound measurement regards to volume traffic, vehicle movement rate, location away from midpoint highway. The peak obtained at...