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Academic literature on the topic 'Box-Jenkins forecasting Computer programs'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Box-Jenkins forecasting Computer programs"

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Zaiyong Tang, Chrys de Almeida, and Paul A. Fishwick. "Time series forecasting using neural networks vs. Box- Jenkins methodology." SIMULATION 57, no. 5 (November 1991): 303–10. http://dx.doi.org/10.1177/003754979105700508.

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Didiharyono, Didiharyono, and Bakhtiar Bakhtiar. "Forecasting Model with Box-Jenkins Method to Predict the Number of Tourists Visiting in Toraja." JEMMA (Journal of Economic, Management and Accounting) 1, no. 1 (October 25, 2018): 62. http://dx.doi.org/10.35914/jemma.v1i1.75.

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This study aims to determine forecasting model with Box-Jenkins method and obtain results of data forecasting the number of tourists visiting in Toraja (Tanah Toraja and North Toraja regency) the future period. Research method used is applied research with quantitative data. Research procedures include identification of model, parameter estimation in model, verification and forecasting with using Minitab computer software. Based on the research obtained four models used in forecasting the number of tourists in Toraja the future period is ARIMA(1,1,1), ARIMA(2,1,1), ARIMA(1,2,1) and ARIMA(2,2,1). The correct criteria in selecting the best model is the model that has the smallest Mean Square (MS) value. In this case the time series model with the smallest MS value is ARIMA(2,2,1) that is 736062253. Thus, this model will used in forecasting is ARIMA(2,2,1) with equations . The forecasting results for January to December 2021 is 149985, 193099, 207559, 202903, 222426, 229294, 239108, 250921, 260701, 271895, 283037 and 294221.
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Hadwan, Mohammad, Basheer M. Al-Maqaleh, Fuad N. Al-Badani, Rehan Ullah Khan, and Mohammed A. Al-Hagery. "A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting." Computers, Materials & Continua 70, no. 3 (2022): 4829–45. http://dx.doi.org/10.32604/cmc.2022.017824.

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Thapa, Rabin, Shivahari Devkota, Sandip Subedi, and Babak Jamshidi. "Forecasting Area, Production and Productivity of Vegetable Crops in Nepal using the Box-Jenkins ARIMA Model." Turkish Journal of Agriculture - Food Science and Technology 10, no. 2 (March 1, 2022): 174–81. http://dx.doi.org/10.24925/turjaf.v10i2.174-181.4618.

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Forecasting of vegetable area, production, and productivity of Nepal was made from the historical data of 1977/78 to 2019/20 by using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models. The best fitted ARIMA models were chosen based on the minimum value of the selection criterion, Akaike information criteria (AIC) and Bayesian information criteria (BIC). ARIMA (0, 2, 1) model was found suitable for all areas and production, whereas ARIMA (0, 2, 0) model was best fitted for forecasting vegetable productivity. The model was cross-validated by comparing the point prediction with the actual test series data from 2015/16 to 2019/20. The performances of models were determined from the mean absolute percent error (MAPE) value. The MAPE was found to be 2.70%, 2.40%, and 3.80%, respectively for the prediction of area, production, and productivity. The forecast was made for the immediate five years (2020/21 to 2024/25), and it showed an increasing value for area and production while the forecasts of productivity had lower values. The vegetable production policy in Nepal should be planned following accurate forecasts to increase production in the upcoming years. Awareness among the vegetable growers should be raised in the following years with appropriate extension programs.
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Sholl, Patricia, and R. Kenneth Wolfe. "The Kalman filter as an adaptive forecasting procedure for use with Box-Jenkins arima models." Computers & Industrial Engineering 9, no. 3 (January 1985): 247–62. http://dx.doi.org/10.1016/0360-8352(85)90005-1.

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Monika, Putri, Budi Nurani Ruchjana, and Atje Setiawan Abdullah. "The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city." International Journal of Data and Network Science 6, no. 4 (2022): 1309–18. http://dx.doi.org/10.5267/j.ijdns.2022.6.004.

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A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated Moving Average (VARIMA) model. In this research, we studied the ARIMA model which is studied with a non-constant error variance. In this case, the Autoregressive Conditional Heteroscedasticity (ARCH) model is applied to outgrow the non-constant error variance. Selection of the best model by examining the minimum AIC for each model. The ARIMA-ARCH model is implemented on rainfall data in Bandung city with Knowledge Discovery in Database (KDD) in Data Mining. The methodology in the KDD process, including pre-processing, data mining process, and post-processing. Based on the results of model fitting, the best model is the ARIMA (2,1,4)-ARCH (1) model. The result of forecasting rainfall in Bandung shows a MAPE value is 11%, which has a similar pattern with actual data for short time 2-4 days. From these results, we conclude that the ARIMA-ARCH model is a good model for forecasting the rainfall in Bandung city.
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Popescu, Th D. "Experiences with a computer aided procedure for time series analysis and forecasting using Box-Jenkins philosophy." Annual Review in Automatic Programming 12 (January 1985): 361–64. http://dx.doi.org/10.1016/0066-4138(85)90062-x.

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Buchori, Mohammad, and Tedjo Sukmono. "Peramalan Produksi Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) di PT. XYZ." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 2, no. 1 (June 25, 2019): 27. http://dx.doi.org/10.21070/prozima.v2i1.1290.

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In production planning and control the first step is to forecast to determine how much production, the company forecasting is still not optimal, because forecasting has an important role in a company. PT. XYZ is a food company that produces chicken meatballs and chicken dumplings. So from that this study uses the forecasting method Autoregressive Integreted Moving Average (ARIMA). ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the forecasting accuracy is not good. The purpose of this research is to get a good ARIMA model, used to forecast production in the company. So that the production becomes optimal and not excessive which can cause waste of raw materials, which will make production costs a lot. Data processing is done with the help of an Eviews computer program to determine a good ARIMA model, from processing data obtained by ARIMA (1.0,0). With the results obtained forecasting in the period 37 to period 48.
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Siqueira, Hugo, Mariana Macedo, Yara de Souza Tadano, Thiago Antonini Alves, Sergio L. Stevan, Domingos S. Oliveira, Manoel H. N. Marinho, et al. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods." Energies 13, no. 16 (August 16, 2020): 4236. http://dx.doi.org/10.3390/en13164236.

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The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.
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Nigam, Bhanuj, and Dr A. C. Shukla. "SALES FORECASTING USING BOX JENKINS METHOD BASED ARIMA MODEL CONSIDERING EFFECT OF COVID -19 PANDEMIC SITUATION." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (November 1, 2021): 87–97. http://dx.doi.org/10.33564/ijeast.2021.v06i07.015.

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This paper presents Box-Jenkins method used to forecast the future demand in a two wheeler industry. An automated technique in machine learning with the help of python language has been developed and used to analyze time series data and ultimately fit the model for future demand projection. The time series data is collected for the Royal Enfield bikes’ monthly sale available at the official website of Eicher motors ltd. The resulting pattern found in time series data is used to forecast the future behavior, knowledge of which will help to maintain the appropriate inventory and to reduce the risk in terms of changing customers preferences, resource availability etc. Also the effect of covid-19 pandemic has been captured to visualize its impact. The results thus obtained will be useful to understand the pattern if it occurs again in future. This method provides superior results and can be widely used in various forecasting scenario.
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Dissertations / Theses on the topic "Box-Jenkins forecasting Computer programs"

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Siqueira, Hugo Valadares 1983. "Máquinas desorganizadas para previsão de séries de vazões." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260686.

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Orientadores: Christiano Lyra Filho, Romis Ribeiro de Faissol Attux
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-24T05:06:09Z (GMT). No. of bitstreams: 1 Siqueira_HugoValadares_D.pdf: 10867937 bytes, checksum: 512652380d6dd25b8717bfd5c8f5f0f8 (MD5) Previous issue date: 2013
Resumo: Este trabalho explora a possibilidade de aplicação de arquiteturas de redes neurais artificiais - redes neurais de estado de eco (ESN) e máquinas de aprendizado extremo (ELM) - aqui denominadas coletivamente por máquinas desorganizadas (MDs), para a previsão de séries de vazões. A previsão de vazões é uma das etapas fundamentais no planejamento da operação dos sistemas de energia elétrica com predominância hidráulica, como é o caso brasileiro. Os modelos mais comumente utilizados para previsão de vazões pelo Setor Elétrico Brasileiro (SEB) são baseados na metodologia Box & Jenkins, lineares, sobretudo modelos periódicos auto-regressivos (PAR). Todavia, técnicas mais abrangentes, que alcancem melhores desempenhos, vêm sendo investigadas. Destacam-se as redes neurais artificiais, sobretudo arquiteturas do tipo perceptron de múltiplas camadas (MLP), muito conhecidas por serem aproximadores universais com elevada capacidade de aprendizado e mapeamento não-linear, características desejáveis para solução do problema em questão. Por outro lado, as máquinas desorganizadas têm apresentado resultados promissores na previsão de séries temporais. Estes modelos têm um processo de treinamento simples, baseado em encontrar os coeficientes de um combinador linear; em particular, não precisam fazer ajuste dos pesos de sua camada intermediária, ao contrário das redes MLP. Por isso, este trabalho investigou as MDs do tipo ESN e ELM, versões recorrente e não-recorrente, respectivamente, para previsão de vazões médias mensais. Serão avaliadas também três técnicas para retirada da componente sazonal característica destas séries ¿ médias móveis, padronização e diferenças sazonal ¿ além da exploração de técnicas de seleção de variáveis do tipo filtro e wrapper, no intuito de melhorar performance dos modelos preditores. Na maioria dos casos estudados, os resultados obtidos pelas MDs na previsão das séries associadas a importantes usinas hidrelétricas brasileiras - Furnas, Emborcação e Sobradinho - em cenários com horizontes variados, mostraram-se de melhor qualidade do que os obtidos pelo modelo PAR e as redes neurais MLPs
Abstract: This work explores the possibility of application of neural network architectures ¿ echo state networks (ESN) and extreme learning machines (ELM) ¿ collectively referred as unorganized machines (UMs), to seasonal streamflow series forecasting. Streamflow forecasting is one of the key steps in the planning of operation of power systems with hydraulic predominance, as in the Brazilian case. The models most commonly used to streamflow prediction by the Brazilian Electric Sector are based on the Box & Jenkins methodology, with linear and especially periodic autoregressive models. However, more extensive techniques that achieve better performances have been investigated to this task. We highlight artificial neural networks, especially architectures such as multilayer perceptron (MLP), known to be universal approximators with high learning ability skills ability to perform nonlinear mapping, desirable characteristics for the solution of this problem. On the other hand, unorganized machines have shown promising results in time series forecasting. These models have a simple training process, based on finding the coefficients of a linear combiner; they do not require adjustments in the weights of the hidden layer, which are necessary with MLP architecture. Therefore, this study investigated the UMs such as ESN and ELM, recurrent and nonrecurrent versions, respectively, to seasonal streamflow series forecasting. Three techniques to remove the seasonal component of streamflow series will also be evaluated - moving averages, standardization and seasonal differences. In addition, In order to improve the performance of predictive models techniques for variable selection, such as filters and wrappers, will also be explored. In the most cases, the computational results obtained by the UMs in streamflow series forecasting associated to important Brazilian hydroelectric plants - Furnas, Emborcação and Sobradinho - with scenarios including several horizons, presented better performance when compared to forecasting obtained with PAR models and MLPs
Doutorado
Energia Eletrica
Doutor em Engenharia Elétrica
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Cechin, Rafaela Boeira. "Análise de previsão de preços de ações de uma carteira otimizada, utilizando análise envoltória de dados, redes neurais artificiais e modelo de box-jenkins." reponame:Repositório Institucional da UCS, 2018. https://repositorio.ucs.br/handle/11338/3660.

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Lerman, David. "A model for the generation and study of electromyographic signals." Thesis, 1991. http://hdl.handle.net/1957/37016.

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A computer model simulating the electrical activity of muscles of the upper arm during elbow motion is presented. The output of the model is an Electromyographic (EMG) signal. System identification is performed on the EMG signals using autoregressive moving average (ARMA) modelling. The calculated ARMA coefficients are then used as the feature set for pattern recognition. Pattern recognition is performed on the EMG signals to attempt to identify which of four possible motions is producing the signal. The results of pattern recognition are compared with results from pattern recognition of real EMG signals. The model is shown to be useful in predicting general trends found in the real data, but is not robust enough to predict accurate quantitative results. Simplifying assumptions about the filtering effects of body tissue, and about the size and position of muscles, are conjectured to be the most likely reasons the model is not quantitatively accurate.
Graduation date: 1992
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Books on the topic "Box-Jenkins forecasting Computer programs"

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Lerman, David. A model for the generation and study of electromyographic signals. 1991.

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Belzer, Jack, Albert G. Holzman, and Allen Kent. Encyclopedia of Computer Science and Technology: Volume 3 - Ballistics Calculations to Box-Jenkins Approach to Time Series Analysis and Forecasting. Taylor & Francis Group, 2021.

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Belzer, Jack, Albert G. Holzman, and Allen Kent. Encyclopedia of Computer Science and Technology: Volume 3 - Ballistics Calculations to Box-Jenkins Approach to Time Series Analysis and Forecasting. Taylor & Francis Group, 2021.

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Belzer, Jack, Albert G. Holzman, and Allen Kent. Encyclopedia of Computer Science and Technology: Volume 3 - Ballistics Calculations to Box-Jenkins Approach to Time Series Analysis and Forecasting. Taylor & Francis Group, 2021.

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Conference papers on the topic "Box-Jenkins forecasting Computer programs"

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Khan, Riaz Ullah, Sardar Muhammad Hussain, Amin Ul Haq, Muhammad Asif, Muhammad Yousaf, Aimel Zafar, Sultan Almakdi, Jianping Li, and Muhammad Anwar Malghani. "Forecasting Time Series COVID-19 Statistical Data with Auto-Regressive Integrated Moving Average and Box-Jenkins' Models." In 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2021. http://dx.doi.org/10.1109/iccwamtip53232.2021.9674126.

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Deethong, Thapanee, and Nathaphon Boonnam. "Forecasting Analysis of the Durian Yield Trends in Southern Thailand Using Holt-Winters Exponential Smoothing Method and Box-Jenkins' Techniques." In 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON). IEEE, 2022. http://dx.doi.org/10.1109/ectidamtncon53731.2022.9720330.

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