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30 results for “time series modeling”
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Research ArticleOpen accessIn the abstractAdvanced Multivariate Time Series Forecasting ModelsMiss Lei WangIn this study, the focus is to collect and summarize various advanced forecasting models for multivariate time series dataset. We have discussed about the inherent forecasting strengths and weaknesses related to these time series modelings. Also, the main section deal with the experience of using such data in econometric analysis. Besides, the implementation of SAS and R softwares improve the … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 21 Dec 2018 · 7,316 accessesView PDFCite
Research ArticleOpen accessIn the abstractHydrological Forecasting Using Hybrid Data-Driven ApproachYoungmin Seo, Sungwon KimThis study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs … …American Journal of Applied Sciences, Vol. 13, Issue 8 · 2 Sep 2016 · 8,136 accessesView PDFCite
EditorialOpen accessIn the abstractNew Kind of Statistical MethodsCagdas Hakan AladagAladag, C.H. and E. Egrioglu, 2012. Advanced Time Series Forecasting Methods. In: Advances in Time Series Forecasting, Aladag, C.H. and E. Egrioglu (Eds.), Bentham Science Publishers, Oak Park, ISBN-10: 1608053733. Aladag, C.H., 2011. A Hybrid Intelligent Technique Combines Neural Networks and Tabu Search Methods for Forecasting, Computer Search Algorithms. 1st Edn., Nova Publisher, ISBN: 978-1-61209-043-6. Aladag, C.H., E Egrioglu, U. … …Journal of Mathematics and Statistics, Vol. 10, Issue 4 · 28 Nov 2014 · 7,588 accessesView PDFCite
Research ArticleOpen accessIn the abstractSeasonal Autoregressive Integrated Moving Average Model for Precipitation Time SeriesYan Wang, Xinghua Chang, Meng Gao, Xiyong HouPredicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from … …Journal of Mathematics and Statistics, Vol. 8, Issue 4 · 20 Feb 2013 · 12,655 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Dynamic Temporal Neuro Fuzzy Inference System for Mining Medical DatabasesR. Sethukkarasi, A. KannanThe analysis and representation of temporal data are becoming increasingly important in many areas of research and application. The existing Fuzzy Cognitive Maps (FCMs) are efficient modeling method for knowledge representation and fuzzy reasoning in time series analysis. In the past, it was used to represent a complex causal system as a collection of concepts and causal relationships among concepts. … …Journal of Computer Science, Vol. 8, Issue 11 · 7 Nov 2012 · 6,358 accessesView PDFCite
Research ArticleOpen accessIn the abstractTime Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive ModelSuhartonoProblem statement: Most of Seasonal Autoregressive Integrated Moving Average (SARIMA) models that used for forecasting seasonal time series are multiplicative SARIMA models. These models assume that there is a significant parameter as a result of multiplication between nonseasonal and seasonal parameters without testing by certain statistical test. Moreover, most popular statistical software such as MINITAB and SPSS only has facility … …Journal of Mathematics and Statistics, Vol. 7, Issue 1 · 29 Jan 2011 · 18,295 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Extension of Yoshida, Imoto, Higuchi and Miyano ResultB. OnoghojobiProblem statement: Different approaches to deal with dynamic model when it is studentized are presented. Approach: In this respect, the recursive formula for calculating state space in the canonical form. Results: The asymptotic distribution of their test under the linear system for the gene network and a studentized-dynamic linear model with Markov denoising switching for estimating time-dependent gene network structure … …Journal of Mathematics and Statistics, Vol. 5, Issue 4 · 31 Dec 2009 · 6,798 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting and Time Series Analysis of Air Pollutants in Several Area of MalaysiaMohd Zamri Ibrahim, Roziah Zailan, Marzuki Ismail, Muhd Safiih LolaProblem statement: In keeping abreast with Malaysia’s rapid economic development and to meet the nation's aspiration for an improved quality of life, clean-air legislation limiting industrial and automobile emissions was adopted in 1978. Approach: Yet, to this day, air pollution from both sources still poses a problem for the nation. In order to predict the status of future air quality … …American Journal of Environmental Sciences, Vol. 5, Issue 5 · 31 Oct 2009 · 7,761 accessesView PDFCite
Research ArticleOpen accessIn the abstractElectricity Load Forecasting based on Framelet Neural Network TechniqueMohammed K. AbdLoad forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This study shows Electricity Load Forecasting modeling based on Framelet Neural Network Technique (FNN) for Baghdad City. Framelet technique is implemented to the time series data, decomposing the data into number of Framelet coefficient signals. The decomposed signals … …American Journal of Applied Sciences, Vol. 6, Issue 5 · 31 May 2009 · 8,274 accessesView PDFCite