Results 1 to 10 of about 70 (64)
Energies, 2023 Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for ...
Babiga Birregah
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Comparison of ARIMA and ARIMA/GARCH Models in EVN Traffic Prediction [PDF]
Journal of Research and Development on Information and Communication Technology, 2014 This paper focuses on building statistical models to capture and forecast the traffic of mobile communication network in Vietnam. Following BoxJenkins method, a multiplicative seasonal ARIMA model is constructed to represent the mean component using the past values of traffic, a GARCH model is then incorporated to ...
Tran Quang Thanh, Trinh Quang Khai
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Prediction Intervals for ARIMA Models [PDF]
Journal of Business & Economic Statistics, 2001 The problem of constructing prediction intervals for linear time series (ARIMA) models is examined. The aim is to find prediction intervals which incorporate an allowance for sampling error associated with parameter estimates. The effect of constraints on parameters arising from stationarity and invertibility conditions is also incorporated.
Snyder, Ralph D. +2 more
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Why Are the ARIMA and SARIMA not Sufficient
CoRR, 2019 The autoregressive moving average (ARMA) model takes the significant position in time series analysis for a wide-sense stationary time series. The difference operator and seasonal difference operator, which are bases of ARIMA and SARIMA (Seasonal ARIMA), respectively, were introduced to remove the trend and seasonal component so that the original non ...
Shixiong Wang +2 more
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Comparison of univariate ARIMA, multivariate ARIMA and vector autoregression forecasting [PDF]
Working paper (Federal Reserve Bank of Cleveland), 1986 A comparison of the forecasting abilities of univariate ARIMA, multivariate ARIMA, and VAR, and examination of whether series should be differenced before estimating models for forecasting purposes.
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Multivariate ARIMA and ARIMA-X Analysis:Package ‘marima’ [PDF]
, 2016 Multivariate arima and arima-x estimation using Spliid's algorithm.
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Abstract Background Dengue remains an enduring public health concern across tropical and subtropical regions of China, with a disproportionate burden observed in economically disadvantaged areas. Dengue outbreaks can overwhelm healthcare systems and impede economic development.
Jingyi Guo +5 more
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Forecasting Wheat Production in Libya Using ARIMA Model-ARIMA
مجلة آفاق للدراسات الإنسانية والتطبيقيةThe wheat crop is a strategic crop in Libya as a food crop and a raw material for some food industries. The study aimed to predict the amount of wheat production in context of Libya during the next six years from 2023-2028. The Auto-regressive Integrated Moving Average (ARIMA) model has been used and relied on Food and Agriculture Organization data ...
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, 2019 Published as part of Opitz, Weston, 2019, Descriptions of new genera and new species of Western Hemisphere checkered beetles (Coleoptera, Cleroidea, Cleridae), pp.
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Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
International Journal of Research in Business and Social Science (2147- 4478)The purpose of this study was to investigate the efficacy of hybrid forecasting models that integrate the classical Autoregressive integrated moving average framework, the support vector machines, the artificial neural networks, and random forest for predicting S&P 500 index returns.
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