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77 articles for “AI-Based Forecasting”
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Research ArticleOpen accessIn the abstractDevelopment of Customer Predictive Model for Investment Using Ensemble Learning TechniqueThongchai Kaewkiriya, Kittipol WisaengMany new investors that want to purchase a fund might be disappointed with the return-on-investment value. This problem occurs because they do not know important factors that could affect the market value. Plus, some assets might not be suitable for the investor's investment style. To reduce the mentioned problems, many asset management companies research the systems that could find a … …Journal of Computer Science, Vol. 19, Issue 6 · 25 May 2023 · 5,477 accessesView PDFCite
Research ArticleOpen accessIn the abstractInvestigation of Factors Influencing the Reproduction of Non-Gregarious Locust Pests in Northern Kazakhstan to Substantiate the Forecast of their Number and Planning of Protective MeasuresKurmet Baibussenov, Aigul Bekbaeva, Valery Azhbenov et al.The article presents the results of many years of research on the phytosanitary state of agricultural land for non-gregarious locust pests. As polyphagous pests, non-gregarious locusts are one of the key destabilizing factors in the production of economically important agricultural plants in the agricultural regions of Northern Kazakhstan. Research on the long-term population dynamics of non-gregarious locust pests to determine … …OnLine Journal of Biological Sciences, Vol. 21, Issue 1 · 17 Mar 2021 · 6,724 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Bangladesh's Inflation through Econometric ModelsNazmul IslamThis research tries to sketch the concrete steps that help carry out to use ARIMA time series models for forecasting Bangladesh’s inflation. The focus, in this paper, is short-term basis annual inflation forecasting. For this purpose, different ARIMA models are used and the candid model is proposed. Based on the diagnostic and evaluation criteria, the most accurate model is selected. … …American Journal of Economics and Business Administration, Vol. 9, Issue 3 · 8 Nov 2017 · 9,199 accessesView PDFCite
Case ReportOpen accessIn the abstractEvaluation of Seasonal Autoregressive Integrated Moving Average Models for River Flow ForecastingKassahun Birhanu Tadesse, Megersa Olumana Dinka, Tena Alamirew, Semu Ayalew MogesReservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for … …American Journal of Environmental Sciences, Vol. 13, Issue 5 · 28 Sep 2017 · 7,891 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Empirical Study of Robust Modified Recursive Fits of Moving Average ModelsMohamed Ali Ismail, Hend A. Auda, Joe W. McKean, Mahmoud Mohamed SadekThe time-series Moving Average (MA) model is a nonlinear model; see, for example. For traditional Least Squares (LS) fits, there are several algorithms to use for computing its fit. Since the model is nonlinear, Fuller discusses a Newton-type step algorithm. proposed a recursive algorithm based on a sequence of three linear LS regressions. In this study, we robustify Koreisha and … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 28 Jul 2022 · 5,861 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrediction of Data Traffic in Telecom Networks based on Deep Neural NetworksQuang Hung Do, Thi Thanh Hang Doan, Thi Van Anh Nguyen et al.Accurate prediction of data traffic in telecom network is a challenging task for a better network management. It advances dynamic resource allocation and power management. This study employs deep neural networks including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) techniques to one-hour-ahead forecast the volume of expected traffic and compares this approach to other methods including Adaptive Neuro-Fuzzy … …Journal of Computer Science, Vol. 16, Issue 9 · 30 Sep 2020 · 8,071 accessesView PDFCite
Research ArticleOpen accessIn the abstractUtilization of Holt's Forecasting Model for Zakat Collection in IndonesiaAkbarizan, Muhammad Marizal, M. Soleh et al.The practice of zakat is gaining popularity in Indonesia. This development is attributed to the strong role of the government in consistently developing zakat infrastructure and the increased awareness of people to practice zakat. Despite this success, a mechanism for predicting future zakat collection has not yet been developed. This study applies Holt's exponential smoothing and Auto-Regressive Integrated Moving Average … …American Journal of Applied Sciences, Vol. 13, Issue 12 · 22 Dec 2016 · 7,501 accessesView PDFCite
Research ArticleOpen accessIn the abstractHydrological Forecasting Using Hybrid Data-Driven ApproachYoungmin Seo, Sungwon KimThis study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs … …American Journal of Applied Sciences, Vol. 13, Issue 8 · 2 Sep 2016 · 8,140 accessesView PDFCite
EditorialOpen accessIn the abstractNew Kind of Statistical MethodsCagdas Hakan AladagAladag, C.H. and E. Egrioglu, 2012. Advanced Time Series Forecasting Methods. In: Advances in Time Series Forecasting, Aladag, C.H. and E. Egrioglu (Eds.), Bentham Science Publishers, Oak Park, ISBN-10: 1608053733. Aladag, C.H., 2011. A Hybrid Intelligent Technique Combines Neural Networks and Tabu Search Methods for Forecasting, Computer Search Algorithms. 1st Edn., Nova Publisher, ISBN: 978-1-61209-043-6. Aladag, C.H., E Egrioglu, U. … …Journal of Mathematics and Statistics, Vol. 10, Issue 4 · 28 Nov 2014 · 7,591 accessesView PDFCite
Research ArticleOpen accessIn the abstractDAILY CRUDE OIL PRICE FORECASTING MODEL USING ARIMA, GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTIC AND SUPPORT VECTOR MACHINESRana Abdullah Ahmed, Ani Bin ShabriCrude oil price forecasting is gaining increased interest globally. This interest is due mainly to the economic value attached to the product. For this reason, new forecasting methods are proposed in the literature. This paper proposes a novel technique for forecasting crude oil price based on Support Vector Machines (SVM). The study adopts the data on crude oil price of … …American Journal of Applied Sciences, Vol. 11, Issue 3 · 11 Jan 2014 · 8,734 accessesView PDFCite