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11 results for “seasonal ARIMA”
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Research ArticleOpen accessPhrase in the titleSeasonal 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 · 10,326 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting Daily Demand in Cash Supply ChainsMichael WagnerProblem statement: Previous studies focused on explaining the long run determinants of currency demand offering limited insight into the short-run determinants and co-variability of daily demand in cash supply chains. Approach: This study contrasted competing techniques of forecasting daily demand in cash supply chains in order to determine the overall performance and the potential of joint forecasting for integrated planning. … …American Journal of Economics and Business Administration, Vol. 2, Issue 4 · 28 Nov 2010 · 7,736 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementing a Hybrid VMD–BiTCN–BiGRU Architecture With Intelligent Optimization for Agricultural Commodity Price Prognostication in Gujarat MarketsJignesh Hirapara, Ratnesh Kumar Namdeo, Ankit Kumar Dubey et al.In the agricultural sector, the prices of pulses require adequate forecasting to plan farming activities, store and market, and guide policy-making. However, the pulse price series is highly volatile, seasonal, nonstationary, and nonlinear, and thus conventional linear time series models are not necessarily very effective. In the suggested framework, the raw price data series is decomposed using Variational Mode Decomposition … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Aug 2026 · 1,021 accessesView PDFCite
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 PDFCite
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
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 abstractModeling 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 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