La presente investigación contiene un análisis de la recaudación tributaria y las predicciones en el Ecuador mediante aplicación modelos series tiempos tomando cuenta presencia COVID-19 medidas tomadas por gobierno central torno a esta emergencia sanitaria. Este se sustenta metodología Box Jenkins para poder identificar modelo que permita incorporar factores influyen establecer pronósticos, lo ...

In an e€ort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. Development of these models is dicult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, a neural net...

INTRODUCTION Box Jenkins’ linear autoregressive integrated moving average (ARIMA) methodology is widely used for analyzing time-series data. Beyond ‘linear’ domain, there are many nonlinear forms to be explored. In fact, nonlinear time-series analysis has been one of the major areas of research in Time-series analysis for more than two decades now. These models are generally more appropriate th...

With the increasing number of geographically dis­ tributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency o...

In this job, short-term forecasts are calculated for the Energy Price in the Electricity Production Market of Spain. The methodology used to achieve these forecasts is based on Artificial Neural Networks, which have been used succesfully in recent years in many forecasting applications. To gauge the quality of forecasts, they have been compared with those obtained with the Box-Jenkins ARIMA mod...

Background The application of the Box-Jenkins autoregressive integrated moving average (ARIMA) model has been widely employed in predicting cases infectious diseases. It shown a positive impact on public health early warning surveillance due to its capability producing reliable forecasting values. This study aimed develop prediction for new tuberculosis (TB) using time-series data from January ...

An algorithm for the identification of a multi-input single-output (MISO) Box-Jenkins model is developed. The method consists of several simple steps: first a high order ARX model is estimated; then the process model is calculated using the so-called Steiglitz-McBride iteration on the filtered inputoutput data; and finally the disturbance model is calculated by estimating an ARMA model of the o...

The aim of this work is to design an alarm system that allows protecting and preventing crop-freezing damages taking decisions with enough time to react. A first step was to obtain a temperature forecast mode. In this line an hourly temperature series was analyzed with Box-Jenkins methodology ( ARIMA models). An alarm system is designed based on these forecast, at each 12 hours, in the air temp...

With the increasing use of deep neural network (DNN) in time series classification (TSC), recent work reveals threat adversarial attack, where adversary can construct examples to cause model mistakes. However, existing researches on attack TSC typically adopt an unrealistic white-box setting with details transparent adversary. In this work, we study a more rigorous black-box detection applied, ...