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33 results for “ARIMA time series”
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Research ArticleOpen accessIn the abstractForecasting of Humidity of Some Selected Stations from the Northern Part of Bangladesh: An Application of SARIMA ModelMd. Moyazzem Hossain, Md. Atikur Rahman, Md. Zahirul Islam, Ajit Kumar MajumderCommunity risk from natural hazards and climate change depends largely on physical and climatic settings of an area, socio-economic condition of a community and the magnitude, duration and consecutiveness of the hazard or change itself. Impacts of climate change can be characterized by increasing temperatures, rainfall, humidity changes and climate related extreme events such as floods, cyclone, droughts, sea level … …American Journal of Environmental Sciences, Vol. 12, Issue 3 · 10 May 2016 · 8,618 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractMODEL BUILDING FOR AUTOCORRELATED PROCESS CONTROL: AN INDUSTRIAL EXPERIENCEM. A. Djauhari, S. L. Lee, Z. IsmailWe show that many time series data are governed by Geometric Brownian Motion (GBM) law. This motivates us to propose a procedure of time series model building for autocorrelated process control that might consist of two steps. First, we test whether the process data are governed by GBM law. If it is affirmative, the appropriate model is directly given by … …American Journal of Applied Sciences, Vol. 11, Issue 6 · 29 Mar 2014 · 6,624 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 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 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 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 PDFCite
Research ArticleOpen accessIn the abstractForecasting Foreign Direct Investment Inflow in Jordan: Univariate ARIMA ModelAl-Abdulrazag Bashier, Bataineh TalalThe present study is an attempt to build a Univariate time series model to forecast the FDI inflows into Jordan over the coming period 2004-2025. The study employs Box-Jenkins methodology of building ARIMA (Autoregressive Integrated Moving Average) model to achieve the goals of the study. An annual sample time series data for the FDI in Jordan was utilized over the … …Journal of Social Sciences, Vol. 3, Issue 1 · 31 Mar 2007 · 8,385 accessesView PDFCite
Research ArticleOpen accessIn the abstractEstimation Techniques for Monitoring and Controlling the Performance of the Computer Communication NetworksIbrahiem M.M. El Emary, Adanan I. Al RabiaThis study was concerned with making a comparative study between four types of time series model with the goal of choosing the optimum one to predict the performance of a computer communication network. The investigated four types of time series are: Least Square (LS), Fourier series, Exponential Weighted Moving Average (EWMA) and the Auto Regressive Integrated Moving Average (ARIMA). Comparative … …American Journal of Applied Sciences, Vol. 2, Issue 10 · 31 Oct 2005 · 7,126 accessesView PDFCite
Review ArticleOpen accessAll words, across the recordThe use of Principal Component Regression and Time Series Analysis to Predict Nitrous Oxide Emissions in GhanaBenjamin Odoi, Lewis Brew, Christopher AttafuahThe disturbing pace of emanation of Nitrous Oxide (N2O) into the atmosphere and its calamitous impact on the environment, as monitored by many governmental agencies and researchers has become a wellspring of worry for many nations and therefore needs due attention. The study deployed nitrous oxide emissions from the three sectors to the total nitrous oxide emissions in Ghana over … …Energy Research Journal, Vol. 12, Issue 1 · 6 Jan 2021 · 8,597 accessesView PDFCite