The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, Seasonality (ETS) modeling have been successfully applied resolve problems with estimation. Our research suggests that it would be ideal use ...

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable cand...

Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthl...

With the vast popularity of deep learning models in engineering and mathematical fields, Artificial Neural Networks (ANN) have recently attracted significant research applications agriculture, economics, informatics finance. In this paper, we use a method to capture predict unknown complex nonlinear characteristics agricultural output based on autoregressive artificial neural network, using Nig...

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis ...

In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a ...

The monsoon rainfall has very important affect on the agricultural production, livestock as well as human ecology. In this study, try to fit Artificial Neural Network (ANN) model to predict average monsoon rainfall. For the ANN models monthly average rainfall, sea surface temperature, wind speed, monsoon rainfall, temperature is used as inputs to predict the monsoon rainfall. The feed forward n...

Wide-spread real-time applications make it necessary for service providers to guarantee QoS parameters. This requires precise forecast of network traffic. A possible method of the forecast is measuring traffic then analyzing it and fitting model to the measured data, finally predicting the observed parameter using the fitted model. The efficiency of the prediction is decreased by outlying sampl...

This study develops a sedimentation process model that simulate the effects of inflow water quality, treatment flow rate and outflow water quality. The model uses transfer function ARIMA (Auto-Regressive Moving Average) for reflecting the dynamic characteristics of the system The sedimentation model for outflow water turbidity are separated into low and high turbidity by input variables, turbid...

objective(s): chitosan, a biodegradable and cationic polysaccharide with increasing applications in biomedicine, possesses many advantages including mucoadhesivity, biocompatibility, and low-immunogenicity. the aim of this study, was investigating the influence of ph, ratio of azelaic acid/chitosan and molecular weight of chitosan on loading efficiency of azelaic acid in chitosan particles. mat...