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80 articles for “Forecasting Models”
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Research ArticleOpen accessIn the abstractTIME SERIES MODELING OF MONTHLY RAINFALL IN ARID AREAS: CASE STUDY FOR SAUDI ARABIANidhal SaadaStochastic techniques are essential in planning and management of water resources systems especially in arid and semi-arid areas where water is scarce. The forecasting of future events requires identifying proper stochastic models to be used in this process. For this purpose, a Periodic ARMA (PARMA) model and a temporal disaggregation models were used in this study to investigate weather they … …American Journal of Environmental Sciences, Vol. 10, Issue 3 · 3 May 2014 · 7,207 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Models for Time Series with Covariates, Trend, Seasonality, Autoregression and OutliersPitsanu Tongkhow, Nantachai KantananthaBayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the … …Journal of Computer Science, Vol. 9, Issue 3 · 16 Apr 2013 · 6,662 accessesView PDFCite
Research ArticleOpen accessIn the abstractSeasonal ARIMA for Forecasting Air Pollution Index: A Case StudyMuhammad Hisyam Lee, Nur Haizum Abd. Rahman, Suhartono et al.Problem statement: Both developed and developing countries are the major reason that affects the world environment quality. In that case, without limit or warning, this pollution may affect human health, agricultural, forest species and ecosystems. Therefore, the aim of this study was to determine the monthly and seasonal variations of Air Pollution Index (API) at all monitoring stations in Johor. … …American Journal of Applied Sciences, Vol. 9, Issue 4 · 14 Feb 2012 · 10,332 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Neuro-Fuzzy Techniques for Solar RadiationW. A. Rahoma, U. Ali Rahoma, A. H. HassanProblem statement: The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with … …Journal of Computer Science, Vol. 7, Issue 10 · 11 Aug 2011 · 7,080 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Approach based on Winter's Model and Weighted Fuzzy Time Series for Forecasting Trend and Seasonal DataSuhartono, Muhammad Hisyam LeeProblem statement: In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first order fuzzy time series model. In this model, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important … …Journal of Mathematics and Statistics, Vol. 7, Issue 3 · 18 Jul 2011 · 7,948 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 · 18,307 accessesView PDFCite
Research ArticleOpen accessIn the abstractSeasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive ModelSuhartono, B. S.S. Ulama, A. J. EndhartaProblem statement: The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly … …American Journal of Applied Sciences, Vol. 7, Issue 10 · 31 Oct 2010 · 7,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Financial Market Annual Performance Measures: Further Evidence+Edward J. Lusk, Michael Halperin, Atanas Tetikov, Niya StefanovaProblem statement: Forecasting is simple; producing accurate forecasts is the essential task. Experience suggests that financial managers often assume that because models used in forecasting are appropriate that they are effective. This study addresses this assumption. Effective is taken to mean forecasts where the Absolute Percentage Error (APE) is equal to or less than 10%. It has been reported that … …American Journal of Economics and Business Administration, Vol. 2, Issue 3 · 30 Sep 2010 · 7,532 accessesView PDFCite
Research ArticleOpen accessIn the abstractConstructing Fuzzy Time Series Model Based on Fuzzy Clustering for a ForecastingAshraf K. Abd Elaal, Hesham A. Hefny, Ashraf H. Abd ElwahabProblem statement: In this study researchers introduced a fuzzy time series model depending on fuzzy clustering to solve the problem in which the membership values are assumed as Song and Chissom model and to increase the performance of fuzzy time series model. Approach: Proposed model employed seven main procedures in time-invariant fuzzy time-series and time-variant fuzzy time series models. In … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 9,751 accessesView PDFCite
Research ArticleOpen accessIn the abstractOn Replenishing Items with Seasonal Intermittent DemandGary Mitchell, Meike NiederhausenProblem statement: There are numerous difficulties associated with replenishing intermittent demand items and these are compounded when the demand distribution(s) vary seasonally. Excess inventories during an “off” season are typical, while shortages frequently occur during the “in” season, especially at the transition points between “seasons”. Approach: Evaluate the extent to which items characterized by non-stationary (seasonal) intermittent demand can be … …American Journal of Economics and Business Administration, Vol. 2, Issue 1 · 31 Mar 2010 · 7,542 accessesView PDFCite