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Relevant bibliographies by topics / Time-series analysis. Box-Jenkins forecasting

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Journal articles

Academic literature on the topic 'Time-series analysis. Box-Jenkins forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Time-series analysis. Box-Jenkins forecasting"

1

Kuru, Merve, and Gulben Calis. "Application of time series models for heating degree day forecasting." Organization, Technology and Management in Construction: an International Journal 12, no. 1 (2020): 2137–46. http://dx.doi.org/10.2478/otmcj-2020-0009.

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AbstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Informatio

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Sriyotha, Sasiwimon, Rojanee Homchalee, and Weerapat Sessomboon. "Forecasting of Production and Consumption of Ethanol in Thailand Using Time Series Analysis." Applied Mechanics and Materials 781 (August 2015): 651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.781.651.

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Ethanol is the important renewable energy in Thailand. It is alcohol produced from sugarcane and tapioca that are agricultural products available in Thailand. Ethanol is used to blend with gasoline for use as gasohol. Ethanol production and consumption in Thailand are fluctuating. Consequently, planning of ethanol production and consumption is irrelevant. In order to solve this problem, this study aims to find forecasting models using time series analysis including exponential smoothing and the Box-Jenkins methods. The most appropriate forecasting model was selected from the two methods by con

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Tsoku, Johannes Tshepiso, Nonofo Phukuntsi, and Lebotsa Daniel Metsileng. "Gold sales forecasting: The Box-Jenkins methodology." Risk Governance and Control: Financial Markets and Institutions 7, no. 1 (2017): 54–60. http://dx.doi.org/10.22495/rgcv7i1art7.

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The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. Furthermore, the prediction accuracy is tested using mean absolute percentage error (MAPE). From the ana

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Purnawansyah, Purnawansyah, Haviluddin Haviluddin, Rayner Alfred, and Achmad Fanany Onnilita Gaffar. "Network Traffic Time Series Performance Analysis Using Statistical Methods." Knowledge Engineering and Data Science 1, no. 1 (2017): 1. http://dx.doi.org/10.17977/um018v1i12018p1-7.

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This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.

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Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Shu Xiu Feng. "Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model." Advanced Materials Research 709 (June 2013): 819–22. http://dx.doi.org/10.4028/www.scientific.net/amr.709.819.

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The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and

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Menezes, Moises Lima de, Reinaldo Castro Souza, and José Francisco Moreira Pessanha. "Electricity consumption forecasting using singular spectrum analysis." DYNA 82, no. 190 (2015): 138–46. http://dx.doi.org/10.15446/dyna.v82n190.43652.

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Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this pape

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Cyprich, Ondrej, Vladimír Konečný, and Katarína Kiliánová. "Short-Term Passenger Demand Forecasting Using Univariate Time Series Theory." PROMET - Traffic&Transportation 25, no. 6 (2013): 533–41. http://dx.doi.org/10.7307/ptt.v25i6.338.

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The purpose of the paper is to identify and analyse the forecasting performance of the model of passenger demand for suburban bus transport time series, which satisfies the statistical significance of its parameters and randomness of its residuals. Box-Jenkins, exponential smoothing and multiple linear regression models are used in order to design a more accurate and reliable model compared the ones used nowadays. Forecasting accuracy of the models is evaluated by comparative analysis of the calculated mean absolute percent errors of different approaches to forecasting. In accordance with the

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Maulana, Hutomo Atman, Muliah Muliah, Maria Zefaya Sampe, and Farrah Hanifah. "Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur." Math Educa Journal 1, no. 2 (2017): 187–99. http://dx.doi.org/10.15548/mej.v1i2.26.

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The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be writt

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Chutiman, Nipaporn, Pannarat Guayjarernpanishk, Monchaya Chiangpradit, Piyapatr Busababodhin, Saowanee Rattanawan, and Butsakorn Kong-Led. "The Forecasting Model with Climate Variables of the Re-emerging Disease Rate in Elderly Patients." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (August 4, 2021): 866–75. http://dx.doi.org/10.37394/232015.2021.17.81.

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This research forecasted the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin in Khon Kaen Province and Roi Et Province in the northeastern part of Thailand. In the study, the time series forecasting with Box-Jenkins Method (SARIMA model) and Box-Jenkins Method with climate variables, i.e total monthly rainfall, maximum average monthly temperature, average relative humidity, minimum average monthly temperature and average temperature (SARIMAX model) was performed. The study results revealed that the forecasting accuracy was closely simil

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Shinkarenko, Volodymyr, Alexey Hostryk, Larysa Shynkarenko, and Leonid Dolinskyi. "A forecasting the consumer price index using time series model." SHS Web of Conferences 107 (2021): 10002. http://dx.doi.org/10.1051/shsconf/202110710002.

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This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result

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