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...

This paper presents a predictive study applied to a manufacturing equipment in order to predict malfunctions, and consequently enabling predictive maintenance practices. ARIMA forecasting methods are successfully compared with neural networks models, both used over data obtained from a monitoring system that continuously keeps track of the relevant equipment parameters. The results show that bo...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

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 ...

As renewable energy increasingly integrates into the electric power system, electric load forecasting and renewable energy power generation forecasting become more important. In this project, ARIMA and NARX are applied to build load forecasting model focusing on improving statistical and computational efficiency without losing accuracy. ARIMA turns out to be better for short term forecasting wh...

Consider the fractional ARIMA time series with innovations that have innnite variance. This is a nite parameter model which exhibits both long-range dependence (long memory) and high variability. We prove the consistency of an estimator of the unknown parameters which is based on the periodogram and derive its asymptotic distribution. This shows that the results of Mikosch, Gadrich, Kl uppelber...

Temperature is one of the main climatic elements that can indicate climate change as climate change seems to be one of the most important issues in the recent two decades. The aim of this research is to study temporal variation in temperature over Dibrugarh city, Assam, India during the period 1981–2010. In this article we are interested in the time series modeling of the average monthly mean t...

In this paper we develop a semi-parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of timeof-day and type-of-day via nonparametric estimation. In addition, an ARIMA model is used t...

With the continuous advancement of machine learning and increasing availability internet-based information, there is a belief that these approaches datasets enhance accuracy price prediction. However, this study aims to investigate validity claim. The examines effectiveness large dataset sophisticated methodologies in forecasting foreign exchange rates (FX) commodity prices. Specifically, we em...