In this study, it was investigated to what extent linear auto regressive models with external input (ARX) and auto regressive moving average models with external input (ARMAX) could be used to describe the inside air temperature of an unheated, naturally ventilated greenhouse under Western European conditions. Outside air temperature and relative humidity, global solar radiation, and cloudiness...

We present a comparative study of electricity consumption predictions using the SARIMAX method (Seasonal Auto Regressive Moving Average eXogenous variables), HyFis2 model (Hybrid Neural Fuzzy Inference System) and LSTNetA (Long Short Time series Network Adapted), hybrid neural network containing GRU (Gated Recurrent Unit), CNN (Convolutional Network) dense layers, specially adapted for this cas...

BACKGROUND Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic....

India is basically an agricultural country and the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. The average annual or seasonal rainfall at a place does not give sufficient information regarding its capacity to support crop production. Rainfall distribution pattern is the most important. The rainfall forecasting is scientificall...

1 Short-term Traffic Flow Forecasting (STFF), the process of predicting future traffic conditions 2 based on historical and real-time observations is an essential aspect of Intelligent Transportation 3 Systems (ITS). The existing well-known algorithms used for STFF include time-series analysis 4 based techniques, among which the seasonal Autoregressive Moving Average (ARMA) model 5 is one of th...

The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which later declared as a global pandemic view its exponential rise and spread around the world. Resultantly, scientific medical research communities globe geared up to curb spread. In this manuscript, authors claim competence AI-mediated methods predict mortality rate. Efficient prediction model enables he...

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...

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 ...

In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving avera...

A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and ...