17 results for “Autoregressive Integrated Moving Average”

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Research ArticleOpen accessIn the abstractRandom Forest Based Feature Selection of Macroeconomic Variables for Stock Market PredictionIsaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam WeyoriA firm’s equity price on the stock-market is reported to be closely related to the Macroeconomic Variable (MVs) of the country in which the firm trades. For this reason, researchers, market traders, financial analysts and forecasters to examine the association between MVs and stock-price have carried out numerous studies, using time-series statistical analysis methods like Autoregressive Integrated Moving Average (ARIMA), … …American Journal of Applied Sciences, Vol. 16, Issue 7 · 31 Jul 2019 · 10,234 accessesView PDF Cite

Research ArticleOpen accessIn the abstractForecasting Ozone Concentrations Using Box-Jenkins ARIMA Modeling in MalaysiaWan Rozita Wan Mahiyuddin, Nur Izzah Jamil, Zamtira Seman et al.Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build Autoregressive Integrated Moving Average (ARIMA) model on the average of monthly ozone data … …American Journal of Environmental Sciences, Vol. 14, Issue 3 · 25 May 2018 · 8,943 accessesView PDF Cite

Research ArticleOpen accessIn the abstractPrediction of Grain Products in TurkeyÖzlem Akay, Gökmen Bozkurt, Güzin YükselMarketing of agricultural products starts with the planning of production on the farm and ends with the sale of food or other goods to manufacturers or consumers. Overall, marketing is a main part of successful agriculture but its significance is usually underestimated, particularly in developing countries. In Turkey, annual variations in grain production are reasonable and result primarily from changes … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 10 Jul 2017 · 7,322 accessesView PDF Cite

Research ArticleOpen accessIn the abstractModeling Positive Time Series Data: A Neglected Aspect in Time Series CoursesMaman A. Djauhari, Lee Siaw Li, Rohayu Mohd SallehSomething has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first whether the data represent a Geometric Brownian Motion (GBM) process. If it is affirmative, unlike … …American Journal of Applied Sciences, Vol. 13, Issue 7 · 1 Jul 2016 · 7,533 accessesView PDF Cite

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 PDF Cite

Research ArticleOpen accessIn the abstractTime Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series AnalysisP.E. Naill M. MomaniProblem statement: Time series analysis and forecasting has become a major tool in different applications in hydrology and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). Approach: In this study we used Box-Jenkins methodology to build ARIMA model for monthly rainfall data … …American Journal of Environmental Sciences, Vol. 5, Issue 5 · 31 Oct 2009 · 8,813 accessesView PDF Cite

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 PDF Cite