Relevant bibliographies by topics / ARIMA/SARIMA

Academic literature on the topic 'ARIMA/SARIMA'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "ARIMA/SARIMA"

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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.

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Seiring berkembangnya zaman, Indonesia memiliki perkembangan yang pesat dalam bidang tranportasi, khusunya transportasi laut yaitu kapal terbang. Arus transportasi laut yang ramai juga dirasakan oleh Pelabuhan Tanjung Perak yang berada di Surabaya. Adanya fluktuasi dan terjadi penurunan jumlah penumpang di Pelabuhan Tanjung Perak pada bulan Februari 2020 akibat munculnya virus COVID-19 yang memberi akibat pada bidang pariwisata dan bidang transportasi untuk menutup sementara beroperasinya sektor tersebut. Adapaun penelitian ini bertujuan untuk memprediksi jumlah penumpang pelayaran dengan metode Autoregressive Integrated Moving Average (ARIMA) dan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Metode ARIMA dan SARIMA memiliki data berfluktuasi yang berbeda pola. Metode ARIMA dengan pola yang menunjukkan fluktuasi yang tidak tetap dan metode SARIMA dengan pola musiman. Dengan prediksi ini diharapkan dapat membantu sektor yang ada di Pelabuhan untuk mengantisipasi kenaikan dan penuruna penumpang, mempersiapkan sarana prasarana, dan menyediakan fasilitas yang memadai. Penelitian ini menganalisis jumlah keberangkatan penumpang pelayaran di Pelabuhan Tanjung Perak sebagai data sekunder dari situs resmi Badan Pusat Statistik dengan periode Januari 2014 sampai dengan November 2021. Model yang terpilih pada metode ARIMA yaitu ARIMA(1,1,1) sedangkan pada metode SARIMA yaitu SARIMA(1,1,1)(2,0,0)12. Hasil penelitian menunjukkan analisis dengan metode ARIMA mempunyai nilai akurasi lebih kecil daripada analisis dengan menggunakkan metode SARIMA yaitu sebesar 16.15% dan merupakan metode terbaik untuk peramalan ini. Kata Kunci: Peramalan, ARIMA, SARIMA
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Vishwakarma, 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.

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Abstract: The purpose of this study is to compare VAR, ARIMA and SARIMA methods in an attempt to generate sales forecasting in Store xyz with high accuracy. This study will compare the results of sales forecasting with time series forecasting model of Vector Auto Regression (VAR), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). VAR or ARIMA model still accurate when the time series data is only in a short period, these models is accurate on short period forecasting but less accurate on long period forecasting. Meanwhile Seasonal Autoregressive Integrate Moving Average is more accurate on forecasting seasonal time series data, either it’s pattern shows trend or not all three models are compared with forecasting data showing seasonal patterns. The data used is the data of super mart retail store, sales from 2017 to 2022. Accuracy level of each model is measured by comparing the percentage of forecasting value with the actual value. This value is called Mean Absolute Deviation (MAD). Based on the comparison result, the best model with the smallest MAD value is SARIMA model (0,1,0) (0,1,0)12 with MAD value 0.122. From the comparison results can be concluded that the SARIMA model is optimal to be used as a model for further forecasting Keywords: Machine Learning, sales prediction, ARIMA, SARIMA, VAR, PYTHON, Anaconda navigator, Jupiter notebook.
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Prendin, 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.

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Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.
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Ningsih, 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.

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Jumlah kedatangan wisatawan mancanegara ke Sumatera Barat melalui Bandara Internasional Minangkabau cenderung mengalami perubahan di setiap tahunnya. Untuk mengetahui jumlah kedatangan wisatawan mancanegara di masa yang akan datang, dapat dilakukan dengan menggunakan model SARIMA. Model SARIMA merupakan model ARIMA yang mengandung unsur musiman. Model ini diaplikasikan untuk meramalkan jumlah kedatangan wisatawan mancanegara pada periode Januari 2019 hingga Desember 2019. Hasil analisis data menunjukkan bahwa model SARIMA(1, 0, 1)(2, 1, 0)12 yang terbaik, dimana hasil pendugaan yang diperoleh tidak jauh berbeda dari data aktual.Kata Kunci: Wisatawan Mancanegara, Model SARIMA, Peramalan
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Othman, 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.

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Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.
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ARUAN, 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.

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Objektif. Peramalan dilakukan untuk memprediksi kejadian yang akan terjadi dimasa depan berdasarkan data masa lalu. Dalam hal ini dilakukan peramalan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan untuk membantu UKM tersebut memprediksi penjualan kelapa di tengah pendemi Covid 19. Peramalan dilakukan untuk memprediksi penjualan kelapa pada bulan Januari -Maret 2021. Selanjutnya, hasil peramalan yang diperoleh akan divalidasi dengan data aktual penjualan kelapa. Validasi ini bertujuan untuk mengetahui apakah metode peramalan yang digunakan sesuai untuk meramalkan penjualan di UKM Pak Balen Pasar Kandak Medan. Jika hasil peramalan dan validasi sesuai maka metode peramalan yang dipilih tepat untuk digunakan sebagai metode peramalan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan. Material and Metode. Metode peramalan yang digunakan untuk meramalkan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan adalah metode ARIMA dan SARIMA. Kemudian dilakukan serangkaian uji untuk memilih metode yang tepat yang terdiri dari uji stasioneritas ragam, uji stasioneritas rata-rata, uji white noise, pemodelan sementara, dan uji signifikansi. Hasil. Berdasarkan pengolahan data penjualan kelapa di UKM Pak Balen Pasar Kandak Medan diperoleh peramalan dengan metode ARIMA yang sesuai untuk meramalkan penjualan kelapa pada bulan Januari – Maret 2021 masing-masing adalah 520 kelapa, 486 kelapa, dan 459 kelapa. Sedangkan, metode SARIMA berdasarkan pengolahan data tidak memenuhi untuk peramalan, karena pada pengolah data uji signifikansi tidak terdapat model SARIMA yang signifikan. Kesimpulan. Metode yang paling sesuai untuk meramalkan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan adalah metode ARIMA dengan model (1, 1, 1). Dari validasi peramalan ARIMA dan penjualan aktual maka dapat disimpulkan bahwa metode ARIMA sesuai untuk meramalkan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan. Karena hasil peramalan ARIMA mendekati data penjualan kelapa aktual pada bulan Januari – Maret 2021 yaitu masing-masing 572 kelapa, 490 kelapa, dan 451 kelapa.
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Ruhiat, 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.

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Pemodelan dan peramalan deret waktu saat ini berkembang dan banyak digunakan di berbagaibidang termasuk di bidang hidrologi. Salah satu parameter hidrologi yang sangat penting adalahdebit sungai. Besaran dan fluktuasi debit sungai pada periode waktu tertentu sangat dipengauhioleh faktor musiman, yaitu musim hujan dan kemarau. Penelitian ini dilakukan untuk mengetahuipengaruh faktor musiman terhadap kemampuan model dalam menirukan dan meramalkan perilakudari data debit sungai. Pemodelan dilakukan berbasis kepada pendekatan metode statistik BoxJenkins, yaitu Autoregressive Integrated Moving Average (ARIMA) dengan melibatkan faktormusiman dalam pemodelannya, yang dikenal dengan model Seasonal Autoregressive IntegratedMoving Average (SARIMA). Model yang digunakan untuk peramalan adalah model yang terbaik,yaitu model yang memenuhi syarat signifikansi parameter, white noise dan memiliki nilai MAPE(Mean Absolute Percentage Error)yang terkecil. Perbandingan dilakukan terhadap hasilperamalan model-model terbaik, masing-masing terbaik dari model SARIMA dan model ARIMAnon musiman, sehingga dapat diketahui pengaruh faktor musiman terhadap hasil pemodelan danperamalan. Hasil analisis menunjukan ternyatamodel SARIMA terbaik lebih layak digunakandaripada model Arima non musiman.
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Perone, 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.

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The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 46 out 48 metrics (in forecasting future values), i.e., on 95.8% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.
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YAHYA, 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.

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The pattern of changes in the Consumer Price Index (CPI) is very important to observe from time to time because it is closely related to economic indicators such as the amount of money in circulation, exchange rates, interest rates, and other economic indicators. This study aims to form a model and predict the Indonesian Consumer Price Index using the SARIMA method. The data used in modeling are monthly CPI data for the period January 2012 to February 2022. The best model for predicting Indonesia's CPI is the SARIMA (0,1,1)(0,1,1)12 model. This study examines the CPI value in January and February 2022 which is not included in the estimation model, the estimation results (108,08 and 108,20) are very close to the actual CPI value issued by the Central Statistics Agency.
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Silalahi, 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.

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The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.
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Dissertations / Theses on the topic "ARIMA/SARIMA"

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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.

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Made available in DSpace on 2014-06-12T17:16:37Z (GMT). No. of bitstreams: 2 arquivo2907_1.pdf: 634979 bytes, checksum: 330e453c0db3f5452e436a3247c47be0 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009
Esta 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
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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.

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Zatloukal, 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.

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Two real time series, one discussing the area of energy, other discussing the area of economy. By the energetic area we will be dealing with the electric power consumption in the USA, by the economic area we will be dealing with the progress of index PX50. We will try to approve the validity of hypothesis that with some test functions we will be able to set down the accidental unit distribution in these two time series.
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Trcka, 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.

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This work concerns the creation of automatized procedure of ARIMA and SARIMA class model choice according to Box-Jenkins methodology and in this connection, also deals with force testing of unit roots and analysis of applying of informatics criteria when choosing a model. The goal of this work is to create an application in the environment R that can automatically choose a model of time array generating process. The procedure is verified by a simulation study. In this work an effect of values of generating ARMA (1,1) model processes parameters is examined, for his choice and power of KPSS test, augmented Dickey-Fuller and Phillips-Peron test of unit roots.
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Leja, 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.

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I denna uppsats har olika metoder för att göra prognoser för ekonomiska tidsserier med säsongsmönster jämförts och utvärderats. Frågan som undersökningen har kretsat kring är: Vilken metod är bäst lämpad för att göra prognoser av tidsserier med säsongsmönster? De metoder som jämförs är säsongsrensningsmetoderna Census II och TRAMO/SEATS, säsongsmodellerna SARIMA och ARIMA med dummyvariabler för säsong samt en metod där medelvärdena från de fyra första metoderna används som prognoser. För att genomföra undersökningen har dessa metoder tillämpats på fyra ekonomiska tidsserier, nämligen: konsumtion, BNP, export samt byggstarter. Resultatet från undersökningen är att säsongsmodellerna är bäst för konsumtionsserien, säsongsrensningsmetoderna är bäst för BNP- och exportserien och den ena säsongsmodellen (SARIMA) är bäst för byggstartsserien medan den andra (ARIMA-dummy) är den sämsta. Val av prognosmetod beror med andra ord på vilken serie som ska prognostiseras.
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Martins, 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/.

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Os processos espaço-temporais vêm ganhando destaque nos últimos anos, em razão do aumento de estudos compreendendo variáveis que apresentam interação entre as dimensões espacial e temporal. Com o objetivo de modelar esses processos, Pfeifer e Deutsch (1980a) propuseram uma extensão da classe de modelos univariados de Box-Jenkins, denominada por modelo espaço-temporal autoregressivo de média móvel (STARMA). Essa classe de modelos é utilizada para descrever dados de séries temporais espacialmente localizadas. Os processos passíveis de modelagem via classe de modelos STARMA são caracterizados por observações de variáveis aleatórias, em que os locais a serem incorporados no modelo são fixos no espaço. A dependência entre as n séries temporais é modelada por meio da matriz de ponderação, de modo que os modelos da classe STARMA expressem cada observação no tempo t e na localização i como uma média ponderada de combinações lineares das observações anteriores e a inovação defasada no espaço e no tempo conjuntamente. Dada a nova classe de modelos, os objetivos deste estudo foram apresentar a classe de modelos STARMA, implentar computacionalmente, no software R, rotinas que permitam a análise de dados espaço-temporais, com as rotinas implementadas estabelecer e testar modelos de séries temporais aos dados de temperaturas mínimas médias mensais de 8 estações meteorológicas situadas no Paraná e comparar a classe de modelos STARMA com a classe de modelos univariados proposta por Box e Jenkins (1970). Com este estudo verificou-se que na apresentação da classe de modelos STARMA há complexidade no conceito de ordens de vizinhança e na identificação dos modelos espaço-temporais. Em relação a criação de rotinas responsáveis pelas análises de dados espaço-temporais observou-se dificuldades em sua implementação, principalmente no momento de estimação dos parâmetros. Na comparação da classe de modelos STARMA, multivariada, com a classe de modelos SARIMA, univariada, constatou-se que ambos os modelos se ajustaram de maneira satisfatória aos dados, produzindo previsões acuradas.
Spatio-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.
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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.

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n the electrical power grid, the power load is not constant but continuouslychanging. This depends on many different factors, among which the habits of theconsumers, the yearly seasons and the hour of the day. The continuous change inenergy consumption requires the power grid to be flexible. If the energy provided bygenerators is lower than the demand, this is usually compensated by using renewablepower sources or stored energy until the power generators have adapted to the newdemand. However, if buffers are depleted the output may not meet the demandedpower and could cause power outages. The currently adopted practice in the indus-try is based on configuring the grid depending on some expected power draw. Thisanalysis is usually performed at a high level and provide only some basic load aggre-gate as an output. In this thesis, we aim at investigating techniques that are able topredict the behaviour of loads with fine-grained precision. These techniques couldbe used as predictors to dynamically adapt the grid at run time. We have investigatedthe field of time series forecasting and evaluated and compared different techniquesusing a real data set of the load of the Swedish power grid recorded hourly throughyears. In particular, we have compared the traditional ARIMA models to a neuralnetwork and a long short-term memory (LSTM) model to see which of these tech-niques had the lowest forecasting error in our scenario. Our results show that theLSTM model outperformed the other tested models with an average error of 6,1%.
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Stitou, 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.

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This study aims to investigate short-to-medium forecasting and simulation of streamflow, water levels, and sediments in Canada using Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models. The methodology can account for linear trends in the time series that may result from climate and environmental changes. A Universal Canadian forecast Application using python web interface was developed to generate short-term forecasts using SARIMA. The Akaike information criteria was used as performance criteria for generating efficient SARIMA models. The developed models were validated by analyzing the residuals. Several stations from the Canadian Hydrometric Database (HYDAT) displaying a linear upward or downward trend were identified to validate the methodology. Trends were detected using the Man-Kendall test. The Nash-Sutcliffe efficiency coefficients (Nash ad Sutcliffe, 1970) of the developed models indicate that they are acceptable. The models can be used for short term (1 to 7 days) and medium-term (7 days to six months) forecasting of streamflow, water levels and sediments at all Canadian hydrometric stations. Such a forecast can be used for water resources management and help mitigate the effects of floods and droughts. The models can also be used to generate long time-series that can be used to test the performance of water resources systems. Finally, we have automated the process of analysis, model-building and forecasting streamflow, water levels, and sediments by building a python-based application easily extendable and user-friendly. Therefore, automating the SARIMA calibration and forecasting process for all Canadian stations for the HYDAT database will prove to be a very useful tool for decision-makers and other entities in the field of hydrological study.
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Putzulu, 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/.

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Nella presente tesi, si è discusso sul corretto trattamento dei dati di posizione, provenienti da una stazione permanente GPS in PPP, per studiarne l’andamento e successivamente elaborarne le previsioni per il futuro. E' stato utlizzato un approccio con la classe del Modelli ARIMA implementati su linguaggio Python.
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Wallentinsson, 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.

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The mobile traffic in cellular networks is increasing in a steady rate as we go intoa future where we are connected to the internet practically all the time in one wayor another. To map the mobile traffic and the volume pressure on the base stationduring different time periods, it is useful to have the ability to predict the trafficvolumes within cellular networks. The data in this work consists of 4G cellular trafficdata spanning over a 7 day coherent period, collected from cells in a moderately largecity. The proposed method in this work is ARIMA modeling, in both original formand with an extension where the coefficients of the ARIMA model are re-esimated byintroducing some user characteristic variables. The re-estimated coefficients produceslightly lower forecast errors in general than a isolated ARIMA model where thevolume forecasts only depends on time. This implies that the forecasts can besomewhat improved when we allow the influence of these variables to be a part ofthe model, and not only the time series itself.
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Book chapters on the topic "ARIMA/SARIMA"

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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.

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Nokeri, 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.

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Barman, 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.

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Mokhtar, 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.

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Kumar, 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.

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Conference papers on the topic "ARIMA/SARIMA"

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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.

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Pramanik, 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.

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Yu, 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.

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The Facebook reactions were used over 300 billion times during their first year of existence. Research on reaction activity is essential especially for the digital marketing purpose. The market needs to understand how Facebook reactions fluctuate to forecast the best period to post advertisements on Facebook that yields the highest number of reactions. In this study, several time-series models are used to forecast the number of Facebook reactions over a certain period for different domains. A comparative study is done to evaluate the performance of each model, in terms of strengths and weaknesses. Keywords: Forecasting, Facebook reactions, time series model, ARIMA, SARIMA
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Permanasari, 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.

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Xinxiang, 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.

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Santos 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.

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Goswami, 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.

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Hedi, 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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