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81 results for “Forecasting Models”
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Research ArticleOpen accessPhrase in the titleSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 7,981 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAdvanced Multivariate Time Series Forecasting ModelsMiss Lei WangIn this study, the focus is to collect and summarize various advanced forecasting models for multivariate time series dataset. We have discussed about the inherent forecasting strengths and weaknesses related to these time series modelings. Also, the main section deal with the experience of using such data in econometric analysis. Besides, the implementation of SAS and R softwares improve the … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 21 Dec 2018 · 7,316 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAnalysis of Artificial Neural Network Point Forecasting Models and Prediction Intervals for Solar Irradiance EstimationAntônio Fabrício Guimarães de Sousa, Helaine Cristina Moraes Furtado, Wilson Negrão Macêdo, Anderson Alvarenga de Moura MenesesAn accurate knowledge on solar irradiance prediction is particularly required for proper development and planning of Photovoltaic (PV) energy systems. The main purpose of the present research is to assess the accuracy of Artificial Neural Networks (ANN) short-term forecast of univariate solar irradiance time series, with conventional point prediction and Prediction Intervals (PIs), comparing models. The Lower Upper Bound Estimation … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 3 · 17 Jul 2020 · 7,440 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting 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 · 9,195 accessesView PDFCite
Case ReportOpen accessEvery word in the titleEvaluation 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 · 7,885 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleParameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow ForecastingMohammad Valipour, Mohammad Ebrahim Banihabib, Seyyed Mahmood Reza BehbahaniIn this study the ability of Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models in forecasting the monthly inflow of Dez dam reservoir located in Teleh Zang station in Dez dam upstream is estimated. ARIMA model has found a widespread application in many practical sciences. In addition, dam reservoir inflow forecasting is done by some methods such … …Journal of Mathematics and Statistics, Vol. 8, Issue 3 · 30 Aug 2012 · 17,288 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting the Spanish Stock Market Returns with Fractional and Non-Fractional ModelsGuglielmo Maria Caporale, Juncal Cunado, Luis A. Gil-AlanaProblem statement: The content of this note was to assess the forecasting accuracy of various models of the Spanish stock market returns. Approach: We use daily data on the IBEX 35 for the time period January 4th, 2001-March 28th, 2006 and employ both fractional and non-fractional models. Results: The results on the prediction errors for the out-of-sample forecasts indicate that … …American Journal of Economics and Business Administration, Vol. 3, Issue 4 · 8 Dec 2011 · 7,316 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSuitability of Volatility Models for Forecasting Stock Market Returns: A Study on the Indian National Stock ExchangeTrilochan Tripathy, Luis A. Gil-AlanaProblem statement: Measuring volatility is an important issue for stock market traders. Also, volatility has been used as a proxy for riskiness associated with the asset. This study aims to compare the different volatility models based on how well they model the volatility of the India NSE. Approach: The study has made use of five models which are Historical/Rolling Window … …American Journal of Applied Sciences, Vol. 7, Issue 11 · 8 Nov 2010 · 7,851 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Air Temperatures Using Time Series Models and Neural-based AlgorithmsMahmoud M. Smadi, Farouq S. MjalliRecently, neural network models have been popular and found useful for forecasting a wide variety of time series data in many disciplines. This is due to their favorable modeling properties of simplicity, fault and noise tolerance and their capability to adapt to process changes. Nevertheless, applications in climatology have been less widespread than other disciplines such as economics. In this … …Journal of Mathematics and Statistics, Vol. 3, Issue 2 · 30 Jun 2007 · 7,272 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric ModelsAbdel K.M. Baareh, Alaa F. Sheta, Khaled A. KhnaifesForecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper … …Journal of Computer Science, Vol. 2, Issue 10 · 31 Oct 2006 · 6,962 accessesView PDFCite