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Статті в журналах з теми "ARIMA/SARIMA"
Putri, Syifania, and A'yunin Sofro. "Peramalan Jumlah Keberangkatan Penumpang Pelayaran Dalam Negeri di Pelabuhan Tanjung Perak Menggunakan Metode ARIMA dan SARIMA." MATHunesa: Jurnal Ilmiah Matematika 10, no. 1 (April 30, 2022): 61–67. http://dx.doi.org/10.26740/mathunesa.v10n1.p61-67.
Повний текст джерелаVishwakarma, Sagar, and Dr S. C. Solanki. "Predicting sales during COVID using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2481–89. http://dx.doi.org/10.22214/ijraset.2022.41822.
Повний текст джерелаPrendin, Francesco, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, and Jorge Bondia. "Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions." Sensors 22, no. 22 (November 10, 2022): 8682. http://dx.doi.org/10.3390/s22228682.
Повний текст джерелаNingsih, Prawati, Maiyastri Maiyastri, and Yudiantri Asdi. "PERAMALAN JUMLAH KEDATANGAN WISATAWAN MANCANEGARA KE SUMATERA BARAT MELALUI BANDARA INTERNASIONAL MINANGKABAU DENGAN MODEL SARIMA." Jurnal Matematika UNAND 8, no. 2 (July 15, 2019): 128. http://dx.doi.org/10.25077/jmu.8.2.128-134.2019.
Повний текст джерелаOthman, Mahmod, Rachmah Indawati, Ahmad Abubakar Suleiman, Mochammad Bagus Qomaruddin, and Rajalingam Sokkalingam. "Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis." Processes 10, no. 11 (November 19, 2022): 2454. http://dx.doi.org/10.3390/pr10112454.
Повний текст джерелаARUAN, SARA SEPTIANA. "The PERBANDINGAN METODE ARIMA DAN SARIMA DALAM PERAMALAN PENJUALAN KELAPA." JAMI: Jurnal Ahli Muda Indonesia 2, no. 2 (December 20, 2021): 79–90. http://dx.doi.org/10.46510/jami.v2i2.82.
Повний текст джерелаRuhiat, Dadang, and Adang Effendi. "PENGARUH FAKTOR MUSIMAN PADA PEMODELAN DERET WAKTU UNTUK PERAMALAN DEBIT SUNGAI DENGAN METODE SARIMA." TEOREMA 2, no. 2 (March 31, 2018): 117. http://dx.doi.org/10.25157/.v2i2.1075.
Повний текст джерелаPerone, Gaetano. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries." Econometrics 10, no. 2 (April 9, 2022): 18. http://dx.doi.org/10.3390/econometrics10020018.
Повний текст джерелаYAHYA, ARYA. "PERAMALAN INDEKS HARGA KONSUMEN INDONESIA MENGGUNAKAN METODE SEASONAL-ARIMA (SARIMA)." Jurnal Gaussian 11, no. 2 (August 28, 2022): 313–22. http://dx.doi.org/10.14710/j.gauss.v11i2.35528.
Повний текст джерелаSilalahi, Desri Kristina. "Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province." JTAM | Jurnal Teori dan Aplikasi Matematika 4, no. 1 (April 24, 2020): 76. http://dx.doi.org/10.31764/jtam.v4i1.1888.
Повний текст джерелаДисертації з теми "ARIMA/SARIMA"
Claudio, Cordeiro Teti Aloisio. "Modelo de previsão da receita tributária : o caso do ICMS no Estado de Pernambuco." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/3786.
Повний текст джерелаEsta dissertação tem como principal objetivo apresentar os modelos de previsão de arrecadação do ICMS, por segmento econômico, para a Secretaria da Fazenda do Estado de Pernambuco, utilizando as técnicas econométricas. Objetiva-se, com essa pesquisa, disponibilizar aos gestores púbicos do Estado mais um modelo de previsão consistente e com certo grau de confiabilidade. Para tanto, utilizou-se da metodologia Box-Jenkins, mais especificamente os modelos: ARIMA - modelo autorregressivo integrado de média móvel, e SARIMA - modelo autorregressivo integrado de média móvel sazonal, e o software RATS (Regression Analyse Time Series). O trabalho apresenta o comportamento da arrecadação de ICMS no Estado e uma revisão da literatura, onde são abordados os principais conceitos teóricos utilizados, bem como uma análise dos resultados obtidos. Conclui-se que o modelo de previsão utilizando séries temporais, em função de sua capacidade preditiva, pode se transformar em um valioso instrumento para auxiliar na elevação da receita tributária no Estado de Pernambuco, dentro da capacidade contributiva de cada contribuinte
Kinene, Alan. "FORECASTING OF THE INFLATION RATES IN UGANDA: : A COMPARISON OF ARIMA, SARIMA AND VECM MODELS." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-49388.
Повний текст джерелаZatloukal, Radomír. "Analýza a předpověď časových řad pomocí statistických metod se zaměřením na metodu Box-Jenkins." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-228167.
Повний текст джерелаTrcka, Peter. "Výstavba lineárnych stochastických modelov časových radov triedy SARIMA – automatizovaný postup." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193057.
Повний текст джерелаLeja, Eliza, and Jonathan Stråle. "Prognoser av ekonomiska tidsserier med säsongsmönster : En empirisk metodjämförelse." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155790.
Повний текст джерелаMartins, Natália da Silva. "Modelos autoregressivos e de médias móveis espaço-temporais (STARMA) aplicados a dados de temperatura." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-09042013-112557/.
Повний текст джерелаSpatio-temporal processes have been highlighted lately, due to the increase of studies approaching variables that present interactions between the spatial and temporal dimensions. In order to model these processes, Pfeifer e Deutsch (1980a) have suggested an extension of the Box-Jenkins univariate model class, named spatio-temporal autoregressive moving-average model (STARMA). This model class is used to describe spatially located time series data. The processes prone to be modeled via the STARMA model class are characterized by observations of random variables, in which the locations to be incorporated in the model are spatially fixed. The dependence between the n time series is modeled through the weighing matrix. So STARMA models express each observation at time t and location i as a weighed mean of linear combinations of the previous observations and the jointly lagged innovation in space and time. Given the new class models, the objectives of this study were to present a class of models STARMA, implentar computationally, in textit R software, routines that allow the analysis of spatio-temporal data with the routines implemented to establish and test models time series data of monthly average minimum temperatures of 8 meteorological stations located in Paraná and compare the class of models STARMA with the class of univariate models proposed by Box e Jenkins (1970). With this study it was found that the presentation of the class of models STARMA no complexity in the concept of ordered neighborhood and identification of spatio-temporal models. Regarding the creation of routines responsible for the analysis of spatio-temporal observed difficulties in its implementation, especially at the time of estimation of parameters. In comparison class STARMA models, multivariate, with the class of SARIMA models, univariate, it was found that both models were adjusted satisfactorily to the data, producing accurate forecasts.
Fagerholm, Christian. "Time series analysis and forecasting : Application to the Swedish Power Grid." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-88615.
Повний текст джерелаStitou, Adnane. "SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39785.
Повний текст джерелаPutzulu, Matteo. "Modelli ARIMA implementati in ambiente Python applicati a serie temporali GNSS." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25884/.
Повний текст джерелаWallentinsson, Emma Wallentinsson. "Multiple Time Series Forecasting of Cellular Network Traffic." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154868.
Повний текст джерелаЧастини книг з теми "ARIMA/SARIMA"
Vogel, Jürgen. "ARIMA- und SARIMA-Modelle." In Prognose von Zeitreihen, 123–43. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06837-0_6.
Повний текст джерелаNokeri, Tshepo Chris. "Forecasting Using ARIMA, SARIMA, and the Additive Model." In Implementing Machine Learning for Finance, 21–50. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7110-0_2.
Повний текст джерелаBarman, Utpal, Asif Ekbal Hussain, Mridul Jyoti Dahal, Puja Barman, and Mehnaz Hazarika. "Time Series Analysis of Assam Rainfall Using SARIMA and ARIMA." In Smart Computing Techniques and Applications, 357–64. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0878-0_35.
Повний текст джерелаMokhtar, Kasypi, Siti Marsila Mhd Ruslan, Anuar Abu Bakar, Jagan Jeevan, and Mohd Rosni Othman. "The Analysis of Container Terminal Throughput Using ARIMA and SARIMA." In Advanced Structured Materials, 229–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89988-2_18.
Повний текст джерелаKumar, Th Shanta, Himanish S. Das, Upasana Choudhary, Prayakhi E. Dutta, Debarati Guha, and Yeasmin Laskar. "Analysis and Prediction of Air Pollution in Assam Using ARIMA/SARIMA and Machine Learning." In Innovations in Sustainable Energy and Technology, 317–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1119-3_28.
Повний текст джерелаТези доповідей конференцій з теми "ARIMA/SARIMA"
Jeronimo-Martinez, Luis Enrique, Raul E. Menendez-Mora, and Holman Bolivar. "Forecasting acute respiratory infection cases in Southern Bogota: EARS vs. ARIMA and SARIMA." In 2017 Congreso Internacional de Innovacion y Tendencias en Ingenieria (CONIITI) [2017 International Congress of Innovation and Trends in Engineering (CONIITI)]. IEEE, 2017. http://dx.doi.org/10.1109/coniiti.2017.8273326.
Повний текст джерелаPramanik, Anik, Salma Sultana, and Md Sadekur Rahman. "Time Series Analysis and Forecasting of Monkeypox Disease Using ARIMA and SARIMA Model." In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2022. http://dx.doi.org/10.1109/icccnt54827.2022.9984345.
Повний текст джерелаYu, Yong Poh, Khai Yin Lim, and Tong Ming Lim. "A Comparative Study on the Time Series Models for Forecasting Facebook Reactions." In International Conference on Digital Transformation and Applications (ICDXA 2020). Tunku Abdul Rahman University College, 2020. http://dx.doi.org/10.56453/icdxa.2020.1012.
Повний текст джерелаPermanasari, Adhistya Erna, Indriana Hidayah, and Isna Alfi Bustoni. "SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence." In 2013 International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2013. http://dx.doi.org/10.1109/iciteed.2013.6676239.
Повний текст джерелаXinxiang, Zhang, Zhou Bo, and Fu Huijuan. "A comparison study of outpatient visits forecasting effect between ARIMA with seasonal index and SARIMA." In 2017 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2017. http://dx.doi.org/10.1109/pic.2017.8359573.
Повний текст джерелаSantos Freire Ferraz, Rafael, Renato Santos Freire Ferraz, Benemar Alencar de Souza, and Mariana Ribeiro Barros de Alencar. "Twenty-four Hours Ahead Solar Irradiance Forecast Based on Artificial Neural Network, ARIMA and SARIMA." In ANAIS DO 14º SIMPóSIO BRASILEIRO DE AUTOMAçãO INTELIGENTE. Galoa, 2019. http://dx.doi.org/10.17648/sbai-2019-111326.
Повний текст джерелаGoswami, Kakoli, and Aditya Bihar Kandali. "Electricity Demand Prediction using Data Driven Forecasting Scheme: ARIMA and SARIMA for Real-Time Load Data of Assam." In 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2020. http://dx.doi.org/10.1109/compe49325.2020.9200031.
Повний текст джерелаHedi, Anny Suryani, and Agus Binarto. "Forecasting the Number of New Cases of COVID-19 in Indonesia Using the ARIMA and SARIMA Prediction Models." In 2nd International Seminar of Science and Applied Technology (ISSAT 2021). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/aer.k.211106.011.
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