25 results for “ARIMA forecasting”

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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 · 7,942 accessesView PDF Cite

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 PDF Cite

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 · 7,751 accessesView PDF Cite

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 · 7,526 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordSeasonal 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 PDF Cite