Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

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...

Peramalan merupakan salah satu bidang penelitian yang aktif artinya sampai saat ini masih terus dilakukan mengenai proses peramalan runtun waktu terkait dengan pengambilan keputusan. Metode berkembang menjadi semakin cepat mengikuti perkembangan zaman dan teknologi komputasi. Terdapat hal menarik dari tersebut ialah perbaikan metode bersifat hybrid, menggabungkan dua jenis atau lebih berbeda, d...

The paper constructs various core inflation measures. These include various trimmed means using disaggregated data and a structural VAR estimate of core inflation for Ireland. The ability of these core inflation measures to forecast future headline inflation is compared using a regression model. An ARIMA model fitted to the headline inflation rate is used as the benchmark forecast. The forecast...

The popularity of lotteries has increased worldwide in the last few years, and so its revenues, making them an useful study case. This paper shows the research done for testing the randomness of the results of three lotteries and to check if there were any patterns, studying them as time series. This research was done using three methods, statistical randomness tests, autoregressive integrated ...

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...