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259 articles for “Time Series”
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Research ArticleOpen accessPhrase in the titleAn Evolving Autoregressive Predictor for Time Series ForecastingDe Z. Li, Wilson Wang, Fathy IsmailAutoregressive (AR) model is a common predictor that has been extensively used for time series forecasting. Many training methods can used to update AR model parameters, for instance, least square estimate and maximum likelihood estimate; however, both techniques are sensitive to noisy samples and outliers. To deal with the problems, an evolving AR predictor, EAR, is developed in this study … …American Journal of Engineering and Applied Sciences, Vol. 8, Issue 1 · 8 Apr 2015 · 6,965 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleFREQUENT CORRELATED PERIODIC PATTERN MINING FOR LARGE VOLUME SET USING TIME SERIES DATAG. M. Karthik, S. KarthikFrequent pattern mining has been a widely used in the area of discovering association and correlations among real data sets. However, discovering interesting correlation relationship among huge number of co-occurrence patterns are complicated, a majority of them are superfluous or uninformative. Mining correlations among large pile of useless information is extraordinarily useful in real-time applications. In this study, we propose … …Journal of Computer Science, Vol. 10, Issue 10 · 30 Jun 2014 · 6,608 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleBayesian 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 · 6,653 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleSeasonal 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 accessPhrase in the titleModeling Tourist Arrivals Using Time Series Analysis: Evidence From AustraliaGurudeo AnandTularam, Victor Siew Howe Wong, Seyed AbdelhamidShobeiriNejadAustralian tourism has a logistic trend as Butler’s model shows. The stagnation has not been reached so opportunities exist to increase tourism. The logistic model predicts 7.2 million tourists in 2015 but time series models of ARIMA and VAR improve the prediction and explain the data. The ARIMA (2, 2, 2) fits well while the VAR lead to Granger causalities … …Journal of Mathematics and Statistics, Vol. 8, Issue 3 · 10 Sep 2012 · 7,857 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleNeural Networks Based Nonlinear Time Series Regression for Water Level Forecasting of Dungun RiverSiti Hajar Arbain, Antoni WibowoThe Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the … …Journal of Computer Science, Vol. 8, Issue 9 · 13 Aug 2012 · 7,792 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleFuzzy Time Series: An Application to Tourism Demand ForecastingMuhammad Hisyam Lee, Maria Elena Nor, Suhartono et al.Problem statement: Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical and strategic decisions. Besides that, government bodies need accurate tourism demand forecasts to plan required tourism infrastructures, such as accommodation site … …American Journal of Applied Sciences, Vol. 9, Issue 1 · 24 Nov 2011 · 8,474 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA 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 PDFCite
Research ArticleOpen accessPhrase in the titleTime 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 accessPhrase in the titleIndex Financial Time Series Based on Zigzag-Perceptually Important PointsAli Selamat, Chawalsak Phetchanchai, Tanzila Saba, Amjad RehmanProblem statement: Financial time series were usually large in size, unstructured and of high dimensionality. Since, the illustration of financial time series shape was typically characterized by a few number of important points. These important points moved in zigzag directions which could form technical patterns. However, these important points exhibited in different resolutions and difficult to determine. Approach: In this … …Journal of Computer Science, Vol. 6, Issue 12 · 2 Nov 2010 · 9,464 accessesView PDFCite