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Academic literature on the topic 'SARIMA model'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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Journal articles on the topic "SARIMA model"

Woro Tri Handayani, Nandia Rani, Martinus Maslim, and Paulus Mudjihartono. "Forecasting of Catfish Sales by Time Series Using the SARIMA method." Jurnal Buana Informatika 11, no. 2 (October 31, 2020): 83. http://dx.doi.org/10.24002/jbi.v11i2.3535.

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Abstrak. Sistem informasi yang mengotomatiskan proses bisnis, terutama dengan persyaratan khusus masih relevan saat ini. Clarias Makmur, sebuah usaha mikro di Indonesia yang membiakkan dan menjual ikan lele menggunakan sistem informasi ini untuk menjalankan penjualan, pengeluaran, modal, dan pelaporan mereka. Penjualan ikan lele sebagai makhluk hidup memiliki ciri khas tersendiri yang menunjukkan pola musiman yang unik. Sebuah model bernama SARIMA (Seasonal Autoregressive Integrated Moving Average) kemudian diusulkan untuk memprediksi penjualan. Lebih lanjut, sistem yang disebut SITRAN dibuat online supaya perusahaan mikro ini beroperasi secara fleksibel. Ada 400 data penjualan yang digunakan dalam metode ini untuk membuat model dan prediksi, sedangkan 100 data lainnya digunakan untuk menguji akurasi metode. Hasil penelitian menunjukkan bahwa SARIMAX (21,2,0) (1,0,0,12) adalah model terbaik yang ditemukan dalam percobaan dan yang memberikan RMSE terkecil. Kata kunci: prediksi; penjualan ikan lele; pola musiman; SARIMA Abstract. An information system that automates a business process, especially with specific requirement is still relevant these days. Clarias Makmur, a micro cooperation in Indonesia that breeds and sells catfish uses such an information system to carry out their sales, expenses, capital and reporting. The sales of catfish as a living creature have their own characteristics showing the unique seasonal pattern. A model named SARIMA (Seasonal Autoregressive Integrated Moving Average) is then proposed to predict the sales. Furthermore the system called SITRAN is made to be online for the cooperation to operate flexibly. There are 400 sales data used for the method to model and predict, while another 100 are used to test the method accuracy. The result shows that SARIMAX(21,2,0)(1,0,0,12) is the best model found in the experiment giving the smallest RMSE. Keywords: prediction; catfish sales; seasonal pattern; SARIMA

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Permatasari, Novia. "Penggunaan Indeks Google Trend Dalam Peramalan Jumlah Pengunjung Taman Rekreasi Selecta Tahun 2020." Seminar Nasional Official Statistics 2021, no. 1 (November 1, 2021): 1019–24. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.993.

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Kota Batu merupakan salah satu daerah potensi pariwisata di Indonesia, dengan salah satu tujuan pariwisata andalan adalah Taman Rekreasi Selecta. Sejak tahun 2016 hingga 2019, Taman Rekreasi Selecta secara konsisten menjadi tempat wisata dengan jumlah pengunjung terbanyak di Kota Batu. Publikasi data kunjungan wisatawan yang hanya dilakukan sekali dalam satu tahun menunjukkan adanya selang waktu antara pengumpulan dan publikasi data, sehingga pemanfaatan data kunjungan wisatawan tersebut kurang maksimal. Permasalahan tersebut dapat diatasi dengan memanfaatkan real-tima data, yaitu big data. Pada penelitian ini, peneliti akan melakukan nowcasting jumlah pengunjung Taman Rekreasi Selecta pada tahun 2020, serta menggunakan Indeks Google Trend (IGT) yang diharapkan mampu meningkatkan akurasi hasil prediksi. Metode peramalan data runtun waktu yang digunakan adalah SARIMA dan SARIMAX dengan IGT sebagai variabel penjelas. Dibandingkan dengan model SARIMA, metode SARIMAX dengan IGT mampu menurunkan nilai MAE data out-sample hingga 32% dan MAPE sebesar 28%. Perbandingan kedua metode juga menunjukkan bahwa hasil peramalan menggunakan SARIMAX dengan IGT lebih mampu menangkap volatilitas data runtun waktu dari pada model SARIMA. Hasil peramalan jumlah pengunjung Selecta tahun 2020 menunjukkan sempat terjadinya penurunan jumlah pengunjung akibat pandemi covid-19 dan kembali naik diakhir tahun 2020.

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Rochayati, Isti, Utami Dyah Syafitri, I. Made Sumertajaya, and Indonesian Journal of Statistics and Its Applications IJSA. "KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT." Indonesian Journal of Statistics and Its Applications 3, no. 1 (February 28, 2019): 18–32. http://dx.doi.org/10.29244/ijsa.v3i1.171.

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Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.

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Amelia, Ririn, Elyas Kustiawan, Ineu Sulistiana, and Desy Yuliana Dalimunthe. "FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (March 21, 2022): 137–46. http://dx.doi.org/10.30598/barekengvol16iss1pp137-146.

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Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variables and seasonal elements, namely the SARIMAX model (Seasonal ARIMA with eXogenous input). Based on the analysis that has been carried out, the prediction of rainfall in Pangkalpinang City, Bangka Belitung Islands Province can be modeled with the SARIMAX model (0,1,3)(0,1,1){12} for monthly rainfall and SARIMAX (0,1,2 )(0,1,3){12} for maximum daily rainfall. When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. If viewed based on the AIC value of the SARIMA model, the SARIMAX model is also more suitable to be used to predict rainfall in Pangkalpinang City.

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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 similar to the model without the climate variables in the combined analysis although such climate variables had relationship with the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin. Therefore, the appropriate model should be the SARIMA model because it is easier for analysis but with higher forecasting accuracy than the SARIMAX model.

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Faris Nasirudin and Abdullah Ahmad dzikrullah. "Pemodelan Harga Cabai Indonesia dengan Metode Seasonal ARIMAX." Jurnal Statistika dan Aplikasinya 7, no. 1 (June 30, 2023): 105–15. http://dx.doi.org/10.21009/jsa.07110.

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Chili is one of the plants favored by the people of Indonesia because Indonesian cuisine is famous for its spicy taste and spices in every food dish. The rise and fall of chili prices in the market are caused by chili farmers whose production decision-making processes are allegedly not handled and supported by a good production and price forecast. Therefore, analysis is needed to see the forecasting of chili prices in Indonesia in the future. The method that researchers use in forecasting in this study is the SARIMA and SARIMAX methods using the variables of rainfall, inflation, and google trend. The analysis shows that the SARIMAX method is the best model for predicting chili prices with a MAPE value of 6.889% compared to a MAPE SARIMA value of 7.630%.

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Wang, H., C. W. Tian, W. M. Wang, and X. M. Luo. "Time-series analysis of tuberculosis from 2005 to 2017 in China." Epidemiology and Infection 146, no. 8 (April 30, 2018): 935–39. http://dx.doi.org/10.1017/s0950268818001115.

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AbstractSeasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA–GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA–GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA–GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.

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Kim, Taereem, Ju-Young Shin, Hanbeen Kim, Sunghun Kim, and Jun-Haeng Heo. "The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models." Water 11, no. 2 (February 21, 2019): 374. http://dx.doi.org/10.3390/w11020374.

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Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.

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Bleidorn, Michel Trarbach, Wanderson De Paula Pinto, Edilson Sarter Braum, Gemael Barbosa Lima, and Claudinei Antonio Montebeller. "MODELAGEM E PREVISÃO DE VAZÕES MÉDIAS MENSAIS DO RIO JUCU, ES, UTILIZANDO O MODELO SARIMA." IRRIGA 24, no. 2 (June 27, 2019): 320–35. http://dx.doi.org/10.15809/irriga.2019v24n2p320-335.

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MODELAGEM E PREVISÃO DE VAZÕES MÉDIAS MENSAIS DO RIO JUCU, ES, UTILIZANDO O MODELO SARIMA MICHEL TRARBACH BLEIDORN1; WANDERSON DE PAULA PINTO2; EDILSON SARTER BRAUN3; GEMAEL BARBOSA LIMA4 E CLAUDINEI ANTONIO MONTEBELLER5 1Pós-graduando em Certificação Ambiental e Consultoria pela Faculdade de Venda Nova do Imigrante (FAVENI), Av. Ângelo Altoé – nº 888 – Santa Cruz, Venda Nova do Imigrante/ES/Brasil, CEP: 29375-000, michelbleidorn@gmail.com; 2Departamento de Ciências Ambientais, Faculdade da Região Serrana (FARESE), Rua Jequitibá – nº 121- Centro, Santa Maria de Jetibá/ES/Brasil, CEP: 29645-000, wandersondpp@gmail.com; 3Mestrando em Meio Ambiente e Recursos Hídricos do Programa de Pós-Graduação em Ciências Florestais, Universidade Federal do Espírito Santo (UFES), Av. Governador Lidemberg – nº 316 – Centro, Jerônimo Monteiro/ES/Brasil, CEP: 29550-000, edilsonsarter@gmail.com; 4Departamento de Ciências Ambientais, Faculdade da Região Serrana (FARESE), Rua Jequitibá – nº 121- Centro, Santa Maria de Jetibá/ES/Brasil, CEP: 29645-000, gemaelbl@yahoo.com.br; 5Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural (INCAPER), BR 101 – km 151 – Bebedouro, Linhares/ES/Brasil, CEP: 29703-900, cmontebeller@yahoo.com.br. 1 RESUMO O presente estudo teve por objetivo modelar e realizar estudo de previsão de uma série temporal de vazões médias mensais do rio Jucu, ES. A metodologia aplicada baseou-se na proposta por Box e Jenkins. O modelo a ser considerado é o SARIMA, por incluir a característica de sazonalidade. A identificação da sazonalidade foi realizada através da análise espectral, e sua comprovação estatística pelo teste G de Fisher. A identificação da ordem dos modelos foi feita através da análise gráfica dos correlogramas. Dentre os modelos candidatos, foram selecionados aqueles que obtiveram os menores valores dos critérios de informação. O modelo escolhido foi o SARIMA (1,0,0)(5,1,0)12, que obteve um bom ajuste à série em estudo. O referido modelo foi utilizado para realizar previsões de vazões médias mensais para 12 meses à frente. O modelo ajustado se mostrou adequado para realizar previsões. Os valores previstos estão em divergência dos observados, enfatizando uma crise hídrica sem precedente na série temporal considerada. Os resultados deste estudo podem ser utilizados pelos gestores e utilizadores deste curso hídrico, por apresentar características importantes, tais como períodos de cheias e de escassez. Ressalta-se que esses modelos podem ser melhorados ao considerar variáveis explicativas como precipitação, conhecido como modelos SARIMAX. Palavras-chave: Recursos Hídricos, Modelagem Hidrológica, SARIMA. BLEIDORN, M.T.; PINTO, W.P.; BRAUN, E.S.; LIMA, G.B.; MONTEBELLER, C.A. MODELLING AND PREVISION OF MONTHLY MEAN FLOW OF JUCU RIVER, ES, USING SARIMA MODEL 2 ABSTRACT This study aimed at modeling and performing a prediction study of a series of monthly mean flows of Jucu River, ES. The applied methodology was based on the proposal by Box and Jenkins. The model to be considered is SARIMA, because it includes the characteristic of seasonality. Seasonality identification was made through spectral analysis, and its statistical verification by Fisher G test. The identification of the order of the models was done through graphic analysis of the correlogram. Among the candidate models, those that obtained the lowest values of the information criteria were selected. The model chosen was SARIMA (1,0,0) (5,1,0)12, which presented a good fit to the series under study. This model was used to forecast monthly average flows for 12 months ahead. The adjusted model was adequate for forecasting. The predicted values differed from those observed, emphasizing an unprecedented water crisis in the time series considered. The results of this study can be used by managers and users of this watercourse, as they present important characteristics such as flood and scarcity periods. It is considered that these models can be improved by considering explanatory variables such as precipitation, known as SARIMAX models. Keywords: Water resources, Hydrological Modelling, SARIMA.

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PRAHLAD SARKAR, PRADIP BASAK, CHINMAYA SUBHRAJYOTI PANDA, DEB SANKAR GUPTA, MRINMOY RAY, and SABYASACHI MITRA. "Prediction of major pest incidence in Jute crop based on weather variables using statistical and machine learning models: A case study from West Bengal." Journal of Agrometeorology 25, no. 2 (May 25, 2023): 305–11. http://dx.doi.org/10.54386/jam.v25i2.1915.

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Jute crop cultivated in Cooch Behar suffers a substantial amount of physical and economical loss every year due to several major insect pest infestation such as Yellow Mite (Polyphagotarsonemus latus Banks) and Jute Semilooper (Anomis sabulifera Guen). Constructed seasonal plots reveal that for Yellow Mite pest incidence is maximum at 55 DAS, while for Jute Semi Looper it is at 45 DAS. Correlation analysis indicate that the weather parameters such as minimum temperature at current week, maximum RH at one week lag, minimum temperature, minimum and maximum RH at two week lag are significantly correlated with the incidence of Yellow Mite, while in case of Jute Semilooper maximum temperature, minimum and maximum RH at two week lag are significantly correlated. Different forecasting models like ARIMA, ARIMAX, SARIMA, SARIMAX and SVR have been fitted and validated using RMSE values. In case of Jute Semilooper, SARIMAX model is found to be the best fitted model followed by SVR and SARIMA. Similarly, for Yellow Mite ARIMAX model produces the least RMSE value followed by SVR and ARIMA. Following successful model validation, forecasting is done for the year 2022 using the best fitted models.

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Dissertations / Theses on the topic "SARIMA model"

Li, Yangyang M. Eng Massachusetts Institute of Technology. "New product forecasting of appliance and consumables : SARIMA model." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/132738.

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Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2018
Cataloged from the PDF version of thesis.
Includes bibliographical references (pages 43-44).
Drinkworks is a joint venture between Anheuser-Busch InBev and Keurig Green Mountain, with a focus on developing an in-home alcohol system that can prepare different alcoholic beverages. The goal of this project is to forecast the demand for their new product, consisting of appliance and pods, without historical data. For appliance forecast, this paper focuses on an operational level model, SARIMA, which is a time series analysis that considers seasonality and has high accuracy in forecasting. The SARIMA model is implemented with grid search in Python via a demand planning tool, which saves client's time. Weighted consumption rate will be utilized with number of appliance sold to forecast future pods sales. SARIMA model proved to be an effective approach for appliance forecast within client's expectation. A systematic way to forecast pods is also proposed and demonstrated. It is hoped that the results presented here can serve as a basis and help the client with their new product launch.
by Yangyang Li.
M. Eng. in Advanced Manufacturing and Design
M.Eng.inAdvancedManufacturingandDesign Massachusetts Institute of Technology, Department of Mechanical Engineering

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Nikolaisen, Sävås Fredrik. "Forecast Comparison of Models Based on SARIMA and the Kalman Filter for Inflation." Thesis, Uppsala universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202204.

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Inflation is one of the most important macroeconomic variables. It is vital that policy makers receive accurate forecasts of inflation so that they can adjust their monetary policy to attain stability in the economy which has been shown to lead to economic growth. The purpose of this study is to model inflation and evaluate if applying the Kalman filter to SARIMA models lead to higher forecast accuracy compared to just using the SARIMA model. The Box-Jenkins approach to SARIMA modelling is used to obtain well-fitted SARIMA models and then to use a subset of observations to estimate a SARIMA model on which the Kalman filter is applied for the rest of the observations. These models are identified and then estimated with the use of monthly inflation for Luxembourg, Mexico, Portugal and Switzerland with the target to use them for forecasting. The accuracy of the forecasts are then evaluated with the error measures mean squared error (MSE), mean average deviation (MAD), mean average percentage error (MAPE) and the statistic Theil's U. For all countries these measures indicate that the Kalman filtered model yield more accurate forecasts. The significance of these differences are then evaluated with the Diebold-Mariano test for which only the difference in forecast accuracy of Swiss inflation is proven significant. Thus, applying the Kalman filter to SARIMA models with the target to obtain forecasts of monthly inflation seem to lead to higher or at least not lower predictive accuracy for the monthly inflation of these countries.

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Jantoš, Milan. "Modelovanie a predpovedanie sezónnych časových radov." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264619.

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In this Master Thesis there are summarized basic methods for modelling time series, such as linear regression with seasonal dummy variables, exponential smoothing and SARIMA processes. The thesis is aimed on modelling and forecasting seasonal time series using these methods. Goals of the Thesis are to introduce and compare these methods using a set of 2184 seasonal time series followed by evaluation their prediction abilities. The main benefit of this Master Thesis is understanding of different aspects of forecasting time series and empirical verification of advantages and disadvantages these methods in field of creating predictions.

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AIDOO, ERIC. "Forecast Performance Between SARIMA and SETAR Models: An Application to Ghana Inflation Rate." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154339.

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In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process. In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then study both in-sample and out-of-sample forecast performance of this model by comparing it with the linear SARIMA model. Based on the in-sample forecast assessment from the linear SARIMA and the nonlinear SETAR models, the forecast measure MAE and RMSE suggest that the nonlinear SETAR model outperform the linear SARIMA model. Also using multi-step-ahead forecast method we predicted and compared the out-of-sample forecast of the linear SARIMA and the nonlinear SETAR models over the forecast horizon of 12 months during the period of 2010:1 to 2010:12. From the results as suggested by MAE and RMSE, the forecast performance of the nonlinear SETAR models is superior to that of the linear SARIMA model in forecasting Ghana inflation rates. Thought the nonlinear SETAR model is superior to the SARIMA model according to MAE and RMSE measure but using Diebold-Mariano test, we found no significant difference in their forecast accuracy for both in-sample and out-of-sample.

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AIDOO, ERIC. "MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS." Thesis, Högskolan Dalarna, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4828.

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Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.

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SILVA, Pollyanna Kelly de Oliveira. "Análise e previsão de curto prazo do vento através de modelagem estatística em áreas de potencial eólico no nordeste do Brasil." Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1414.

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Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-08-13T15:28:50Z No. of bitstreams: 1 POLLYANNA KELLY DE OLIVEIRA SILVA - TESE (PPGMet) 2017.pdf: 11004478 bytes, checksum: 0d5e098181f432beffc2fd8155027f1e (MD5)
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O vento como fonte para geração de energia elétrica é analisado neste trabalho através de sua variabilidade e da obtenção de previsões de curto prazo para o ano de 2010, período de atuação de El Niño-Oscilação Sul (ENOS) moderado. Modelos de séries temporais propostos por Box-Jenkins e o indicador de desempenho de predição MMREE são usados para obter as melhores estimativas da velocidade do vento com base nas séries observadas. São utilizados dados anemométricos do Projeto SONDA situado às margens do Rio São Francisco em Petrolina – PE, e de dois parques eólicos localizados no litoral do Estado do Ceará: Quixaba (litoral leste), na cidade de Aracati, e Lagoa Seca (litoral oeste), na cidade de Acaraú. O ciclo diário do vento tem velocidades mais baixas (altas) no período da madrugada-início da manhã (pela manhã e final da noite, com exceção do litoral oeste, cujas máximas ocorrem no final da tarde). Um cisalhamento vertical negativo, no vento local, é observado em períodos distintos do dia nas três áreas de estudo. No Ceará ele ocorre no período da manhã (início da tarde e meio da noite) no litoral leste (oeste) e no Lago de Sobradinho durante a noite até o início da manhã. Foi observado que no litoral leste os ventos são mais fortes, provavelmente devido à curvatura côncava do litoral. As estimativas da velocidade do vento no horizonte de 24 horas pelo modelo SARIMA, com dados horários dos 30 dias anteriores ao dia da previsão para treino (Caso 2), mostraram redução nos erros e melhora significativa na série estimada no período da madrugada-início da manhã; no Lago de Sobradinho essas estimativas são mais precisas, quando comparadas àquelas feitas com base em toda a série de dados (Caso 1). Os resultados indicam que o modelo SARIMA com período de entrada de dados menor pode ser aplicado para a previsão da velocidade do vento em áreas de potencial eólico, dando suporte ao operador da rede elétrica na programação da geração despachável para o dia seguinte.
The wind as a source for power generation is analyzed in this work by means of its variability and short-range wind forecasts for the year of 2010, period of moderate El Niño-Southern Oscillation (ENSO). Time series models proposed by Box-Jenkins and the indicator of forecast accuracy MMREE are used to obtain the best wind speed estimates based on the observed series. Anemometric data of the SONDA Project located on the shore of the São Francisco River in Petrolina-PE, and of two wind power plants located on the coast of the Ceará State, Quixaba (east coast), in the city of Aracati, and Lagoa Seca (west coast), in the city of Acaraú, are used. The daily wind cycle has lower (higher) speeds in late night-early morning (in the morning and end of the night, with exception of the west coast, whose maxima occur in late afternoon). A negative vertical shear in the local wind is observed in distinct periods of the day in the three study areas. In Ceará it occurs in the morning (early afternoon and middle of the night) on the east (west) coast and on Sobradinho Lake at night until early in the morning. It was observed that the winds are stronger on the east coast, probably due to the coast’s concave curvature. The wind speed estimates in a 24-hour horizon by the SARIMA model, with hourly data of the 30 days that precede the forecast day for training (Case 2), showed reduction in the errors and significant improvement in the estimated series in late night-early morning; in Sobradinho Lake these estimates are more accurate, as compared to the estimates based on the entire data series (Case 1). The results indicate that the SARIMA model with horter time series as input may be applied to forecast wind speed in areas of eolic potential, giving support to the system operator in programming the dispatchable distributed generation for the next day.

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Han, Jianfeng, and 韩剑峰. "Comparing the performance of SARIMA and dynamic linear model in forecasting monthly cases of mumps in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193789.

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Background To provide a reliable forecast of a disease is one of the main purpose of public health surveillance system. Basic information obtained from data collection can provide the nature knowledge of and the history pattern of a disease. In public health surveillance system, a lot of data are time series, especially for infectious diseases. SARIMA method and DLM method are both applicable tools for time series data analysis. Hong Kong has a relative low mumps prevalence. And the prevalence followed an increasing trend until 2004and kept stable after 2006. However, outbreaks may be also occurred occasionally in developed countries. Method This paper constructs SARIMA models and DLM models of monthly cases of mumps in Hong Kong based on 7 different modeling periods respectively. Then these models were used to predicting the mumps cases in each corresponding forecasting period. The forecasting performance of SARIMA models and DLM models are compared with visualization of the predicting values and three forecasting error measures: MAD, MSE, and MAPE. A forecasting of mumps cases during 2013. 07 and 2014.06 will be made with the method with better forecasting performance of mumps cases in Hong Kong Result For intervals 2009. 01 to 2009. 02, 2011. 01 to 2011. 12, and 2012. 01 to 2012. 12, the forecasts of DLM models have smaller forecasting error measures and are more closely to the real observed values. And the visualization predicting values of SARIMA and DLM models are closely for forecasting intervals 2008 and 2010, where SARIMA forecasts own smaller forecasting error measures. Compare with that based on fitting period 1997 to 2012, the forecasts obtained by the SARIMA model based on fitting period 2006 to 2012 are more close to the real observations. Both SARIMA models and DLM models based on fitting period 1997 to 2003 underestimate the observed value of 2004. 05 to 2004. 12. Conclusion DLM modeling method presents a better performance on forecasting the monthly cases of mumps in Hong Kong. And DLM method is more appropriate to be applied on the analysis of time series with count data and the research of diseases with small counts. And both SARIMA and DLM method are appropriate for analyses based on long time trend. But they are not appropriate to be applied as short time monitor tools. From the result of time series decomposition analysis result the mumps cases had a seasonal pattern, and shows that between July and the next January, the seasonal impact will contribute to the increase of case number of mumps. So it is highly suggest to recommend people under risk to practice more prevention measures to protect them against mumps infectious during that period.
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Helman, Karel. "Statistická analýza teplotních a srážkových časových řad v České republice v období 1961 - 2008." Doctoral thesis, Vysoká škola ekonomická v Praze, 2005. http://www.nusl.cz/ntk/nusl-96401.

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The present dissertation deals with an analysis of monthly time series of average temperatures and precipitation sums recorded at 44 sites in the Czech Republic over the period of 1961--2008. The main research purpose is to acquire deeper knowledge of regularities in the climatic time series development, using an appropriate set of statistical methods. A secondary objective is to search and find correlations between the research outcomes and basic geographic coordinates (altitude, longitude and latitude) of particular measurement stations and comparing all the results achieved for the selected climatic elements. There are two major contributions of this work. In the first place, it presents new knowledge in the field of climatic time series, particularly in connection with the strength and development of their seasonal component, further for instance analysing the relation between the distribution of a residual component and the geographic coordinates of the measurement stations. Another contribution lies in an extensive application of statistical methods of climatic time series analysis. Several types of methods were used, having employed both widely and rarely applied statistical tools (linear trends analysis and Box-Jenkins methodology respectively) as well as those used for the very first time (moving-seasonal time series).

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Robertson, Fredrik, and Max Wallin. "Forecasting monthly air passenger flows from Sweden : Evaluating forecast performance using the Airline model as benchmark." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242764.

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In this paper two different models for forecasting the number of monthly departing passengers from Sweden to any international destination are developed and compared. The Swedish transport agency produces forecasts on a yearly basis, where net export is the only explanatory variable controlled for in the latest report. More profound studies have shown a relevance of controlling for variables such as unemployment rate, oil price and exchange rates. Due to the high seasonality within passenger flows, these forecasts are based on monthly or quarterly data. This paper shows that a seasonal autoregressive integrated moving average model with exogenous input outperforms the benchmark model forecast in seven out of nine months. Thus, controlling for oil price, the SEK/EUR exchange rate and the occurrence of Easter reduces the mean absolute percentage error of the forecasts from 3,27 to 2,83 % on Swedish data.

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Norambuena, Ortega Ramón Simón Andrés. "Predicción de Corto Plazo de Potencia Generada en un Aerogenerador Usando Modelo Sarima." Tesis, Universidad de Chile, 2011. http://www.repositorio.uchile.cl/handle/2250/104196.

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El aumento del aporte energético por parte de las centrales eólicas dentro de la matriz de energías renovables no convencionales de Chile, crea la imperiosa necesidad de desarrollar herramientas que ayuden a gestionar el funcionamiento de parques eólicos, y en particular de los aerogeneradores que lo componen, con el fin de hacer más eficiente la integración y manejo en el sistema interconectado. En esta línea, el propósito de este trabajo es desarrollar un modelo predictivo para la potencia generada en un aerogenerador en base a series de tiempo históricas de variables atmosféricas del lugar donde éste se encuentra. El trabajo de memoria presenta los resultados de la implementación de un modelo SARIMA (siglas en inglés de Seasonal Auto Regressive Integrated Moving Average) y un modelo de persistencia, para predicción de velocidad de viento a horizontes de tiempo de uno y cinco pasos en una escala de tiempo de una hora por cada paso, resultados que luego son transformados a potencia eléctrica por medio de la curva de potencia del aerogenerador considerado. La investigación conecta los campos de la física, generación de energía y de teoría de estimación. Mientras que el primero aporta las ecuaciones con las cuales se describe el viento en la atmósfera y el segundo aporta la base técnica con la cual se relaciona la velocidad del viento con la potencia generada por un aerogenerador, el tercero entrega las herramientas para poder realizar predicción a distintos horizontes por medio de series de tiempo. Por ello, el reporte comienza por los fundamentos físicos que describen la velocidad del viento en la atmósfera, para seguir con los principios técnicos de un aerogenerador y continúa mencionando técnicas utilizadas en el ámbito de la predicción. Además, se trabaja con datos muestreados durante el año 1990 en la localidad de Punta Lengua de Vaca y que fueron obtenidos por el proyecto EOLO del Departamento de Geofísica de la Universidad de Chile. Los resultados de este trabajo permitieron conocer las limitaciones, ventajas y desventajas que poseen tanto el modelo de persistencia como los modelos SARIMA en el ámbito de predicción. En la misma línea, se cuantificó por medio de indicadores de desempeño la exactitud en las predicciones realizadas usando ambos modelos, para finalmente compararlos bajo distintos horizontes de predicción y usando datos de distintos lugares. Finalmente se concluye que el modelo SARIMA puede ser utilizado para predicción de potencia generada en un aerogenerador y que, en comparación con el modelo de persistencia, presenta mejores resultados en predicción a cinco pasos, pero no así en el caso de predicción a un paso, donde la relación se invierte.

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Books on the topic "SARIMA model"

Daerah, Jawa Tengah (Indonesia) Badan Koordinasi Penanaman Modal. Profil penanaman modal SDA dan sarana prasarana. Semarang: Badan Penanaman Modal, Propinsi Jawa Tengah, 2003.

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Anwar, Yusuf. Pasar modal sebagai sarana pembiayaan dan investasi. Bandung: Alumni, 2005.

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Sutedi, Adrian. Pasar modal syariah: Sarana investasi keuangan berdasarkan prinsip syariah. Rawamangun, Jakarta: Sinar Grafika, 2011.

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Indonesia. Bagian Proyek Sistem Indikator Mutu Sekolah Dasar dan Madrasah Ibtidaiyah. Model kebutuhan sarana-prasarana sekolah dasar dan madrasah ibtidaiyah (SD-MI) nasional tahun 1995/1996. [Jakarta]: Departemen Pendidikan dan Kebudayaan, Badan Penelitian dan Pengembangan Pendidikan dan Kebudayaan, Pusat Informatika, 1995.

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Andayani, Tri Rejeki. Strategi pengembangan living values education melalui model pembelajaran nilai toleransi berbasis budaya "tepa sarira" pada anak usia sekolah dasar: Suatu alternatif pendidikan karakter : integrasi nasional dan harmoni sosial = nation integration & social harmony : laporan pelaksanaan hibah kompetitif penelitian strategis nasional. Surakarta]: Universitas Sebelas Maret, 2010.

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Indonesia, BP Mitra Usaha, ed. Panduan pengembangan materi pembelajaran dan standar sarana dan prasarana sekolah menengah kejuruan: Madrasah aliyah, SMA/MA/SMK/MAK : dilengkapi pengembangan program muatan lokal, sistem penilaian KTSP panduan penyelenggaraan pembelajaran pengayaan, sistem penilaian KTSP panduan penyelenggaraan pembelajaran remedial, sistem penilaian KTSP panduan penyelenggaraan pembelajaran tuntas (mastery learning), pengembangan model pembelajaran tatap muka, penugasan restruktur dan tugas mandiri tidak restruktur, panduan pengembangan bahan ajar, panduan pengembangan indikator, panduan umum pengembangan silabus. Jakarta: Mitra Usaha Indonesia, 2008.

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BP, Mitra Usaha Indonesia, ed. Panduan pengembangan materi pembelajaran dan standar sarana dan prasarana sekolah menengah kejuruan: Madrasah aliyah, SMA/MA/SMK/MAK : dilengkapi pengembangan program muatan lokal, sistem penilaian KTSP panduan penyelenggaraan pembelajaran pengayaan, sistem penilaian KTSP panduan penyelenggaraan pembelajaran remedial, sistem penilaian KTSP panduan penyelenggaraan pembelajaran tuntas (mastery learning), pengembangan model pembelajaran tatap muka, penugasan restruktur dan tugas mandiri tidak restruktur, panduan pengembangan bahan ajar, panduan pengembangan indikator, panduan umum pengembangan silabus. Jakarta: Mitra Usaha Indonesia, 2008.

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Kartika, Septi Budi. Modul Praktikum Fisika Dasar. Umsida Press, 2016. http://dx.doi.org/10.21070/2016/978-979-3401-82-9.

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Laboratorium IPA disiapkan dan dikembangkan oleh FKIP untuk mendukung proses belajar mengajar yang berkenaan dengan mata kuliah Sains untuk Program studi Pendidikan IPA. Laboratorium yang terdiri dari laboratorium fisika, kimia, dan biologi ini, merupakan sarana penting untuk pendidikan dan penelitian yang akan menerapkan serta mengembangkan teori-teori dan konsep-konsep dasar dalam bidang fisika, kimia, dan biologi yang terkait. Laboratorium IPA digunakan untuk melaksanakan 15 mata praktikum yaitu: Fisika Dasar, Kimia Dasar, Biologi Umum, Fluida, Makhluk Hidup dan Kehidupan, Interaksi Antar Faktor Fisik, Interaksi Antar Makhluk Hidup, Gerak dan Perubahan, Gelombang dan Optik, Sains Lingkungan Teknologi dan Masyarakat, Zat dan Energi, Kehidupan Tingkat Sel, Larutan, Ilmu Lingkungan, Metabolisme dan Pengendaliannya.

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Studi eksperimental model pembelajaran berbasis portofolio dalam mata pelajaran PPKN sebagai sarana pendidikan demokrasi: Studi kasus di sekolah model : laporan penelitian. [Bandung]: Fakultas Pendidikan Ilmu Pengetahuan Sosial, Universitas Pendidikan Indonesia, 2001.

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Book chapters on the topic "SARIMA model"

Tahyudin, Imam, Berlilana, and Hidetaka Nambo. "SARIMA Model of Bioelectic Potential Dataset." In Communications in Computer and Information Science, 367–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96292-4_29.

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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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Sun, Susu, Xinbo Ai, and Yanzhu Hu. "Emergency Response Technology Transaction Forecasting Based on SARIMA Model." In Lecture Notes in Electrical Engineering, 561–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38460-8_62.

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Batista da Silveira, Tiago, Felipe Augusto Lara Soares, and Henrique Cota de Freitas. "Fast and Efficient Parallel Execution of SARIMA Prediction Model." In Enterprise Information Systems, 217–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75418-1_11.

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Zhang, Xiao. "Forecast and Analysis of China’s CPI Based on SARIMA Model." In Atlantis Highlights in Intelligent Systems, 1354–61. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-030-5_135.

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Sulistiyono, Heri, Faisal Irshad Khan, Humairo Saidah, Ery Setiawan, I. Wayan Yasa, I. Wayan Suteja, Salehudin, and I. Dewa Gede Jaya Negara. "The Development of the SARIMA Model for Flood Disaster Resilience." In Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Civil and Architecture), 211–22. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-088-6_21.

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Kumar, Vipin, Nitin Singh, Deepak Kumar Singh, and S. R. Mohanty. "Short-Term Electricity Price Forecasting Using Hybrid SARIMA and GJR-GARCH Model." In Networking Communication and Data Knowledge Engineering, 299–310. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4585-1_25.

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Zheng, Chen, Yuzhou Wu, Zhigang Chen, Kun Wang, and Lizhong Zhang. "A Load Forecasting Method of Power Grid Host Based on SARIMA-GRU Model." In Communications in Computer and Information Science, 135–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7443-3_9.

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Dedovic, M. Muftic, Samir Avdaković, Adnan Mujezinović, and N. Dautbasic. "The Hybrid EMD-SARIMA Model for Air Quality Index Prediction, Case of Canton Sarajevo." In Advanced Technologies, Systems, and Applications V, 139–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54765-3_9.

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Quan, Pham Dinh, Vu Hoang Anh, Nguyen Quang Dat, and Vijender Kumar Solanki. "Hybrid SARIMA—GRU Model Based on STL for Forecasting Water Level in Red River North Vietnam." In Machine Learning and Mechanics Based Soft Computing Applications, 151–62. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6450-3_16.

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Conference papers on the topic "SARIMA model"

Ogino, Yuki, Yasuyuki Satoh, and Osamu Sakata. "Forecasting Bowel Sound Occurrence Frequency by SARIMA Model." In 2019 23rd International Computer Science and Engineering Conference (ICSEC). IEEE, 2019. http://dx.doi.org/10.1109/icsec47112.2019.8974803.

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Huang, Wuzhe, Fa Si, Feifei Han, Jiahao Liu, Jingshi Zheng, and Yuwen Wei. "Global temperature prediction based on SARIMA+LSTM model." In 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), edited by Xuebin Chen and Hari Mohan Srivastava. SPIE, 2023. http://dx.doi.org/10.1117/12.2686399.

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Shimizu, Shuto, and Sanggyu Shin. "Applicability of SARIMA Model in Tokyo Population Migration Forecast." In 2021 14th International Conference on Human System Interaction (HSI). IEEE, 2021. http://dx.doi.org/10.1109/hsi52170.2021.9538690.

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Ji, Xiaomei, Jingchao Sun, and Haihong Ma. "Call Forecasting Based on SARIMA and SVM Hybrid Model." In 2011 International Conference on Internet Technology and Applications (iTAP). IEEE, 2011. http://dx.doi.org/10.1109/itap.2011.6006285.

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Zhao, Heng, Xumin Zuo, and Peisong Lin. "Sales forecasting for Chemical Products by Using SARIMA Model." In ICBDE'22: The 2022 5th International Conference on Big Data and Education. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3524383.3524396.

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Bouhaddour, Samya, Chaimae Saadi, Ibrahim Bouabdallaoui, Fatima Guerouate, and Mohammed Sbihi. "Tourism in Singapore, prediction model using SARIMA and PROPHET." In VII INTERNATIONAL CONFERENCE “SAFETY PROBLEMS OF CIVIL ENGINEERING CRITICAL INFRASTRUCTURES” (SPCECI2021). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0131288.

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Dong, Chunyao, Jing Liu, Yi Lu, and Long Zhang. "Stock Value Prediction Based on Merging SARIMA Model and Monte Carlo Model." In IC4E 2022: 2022 13th International Conference on E-Education, E-Business, E-Management, and E-Learning. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514262.3514337.

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Fu, Yuan. "Research on Supply and demand matching model based on SARIMA-BP model." In EBIMCS: 2022 5th International Conference on E-Business, Information Management and Computer Science. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3584748.3584787.

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Cabrera, Nestor Gonzalez, G. Gutierrez-Alcaraz, and Esteban Gil. "Load forecasting assessment using SARIMA model and fuzzy inductive reasoning." In 2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2013. http://dx.doi.org/10.1109/ieem.2013.6962474.

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Wang, Junqi, and Xuecheng Wang. "Analysis and Forecast of Shrimp Price Based on SARIMA Model." In Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China. EAI, 2023. http://dx.doi.org/10.4108/eai.9-12-2022.2327622.

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Reports on the topic "SARIMA model"

Sarkissian, Angie. Comparison between the Tap Model and Sara-2d Results. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada329259.

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Gregory, M. V. Technical bases of the second generation SARIS core model (Task Number: 90-008-0). Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/10165492.

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Anderson, B. Technical Review Report for the "Justification for Small Gram Quantity Contents" Safety Analysis Report for Packaging Model 9977-96, Addendum 3, S-SARA-G-00006, Revision 4. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/1124840.

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Studsrød, Ingunn, Ragnhild Gjerstad Sørensen, Brita Gjerstad, Patrycja Sosnowska-Buxton, and Kathrine Skoland. “It’s very complex”: Professionals’ work with domestic violence (DV): Report – FGI and interviews 2022. University of Stavanger, November 2022. http://dx.doi.org/10.31265/usps.249.

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This study explores Norwegian professionals' experiences of working within partner violence (PV) prevention area, including, cross-sectoral and interdisciplinary cooperation as well as possible successful strategies and measures in this area. This report is one of the deliverables of the “Integrated System of Domestic Violence Prevention” (ISDVP) project and of the agreement with The State Treasury, the Institute of Justice in Warsaw, Poland. This study contributes to research on professionals’ experiences of interprofessional collaboration in the domestic violence prevention area – a similar study was conducted in Poland. To facilitate an interdisciplinary and interagency group discussion, five focus groups (with 19 participants) were conducted. The analysis reveals that there is inter- and intra-sectoral collaboration in the domestic violence prevention area. There are marked challenges but also notable success stories. The participants talked about several barriers to cross-sectoral collaborations, such as i) professional requirements of confidentiality, mandate, and/or duty to report, especially in the domestic violence prevention stages; ii) the complexity and plethora of practical and organizational measures and initiatives as well as who does what and when, particularly when helping a client navigate through the system; and iii) the difficulty in defining as well as uncovering domestic violence because it can be understood differently by various parties, especially from a cross-cultural perspective and gender stereotypes. In terms of effective management of multisectoral collaboration, the participants mentioned i) several tools and models, e.g., SARA and Flexid, and ii) organization of emergency shelters; and iii) networking. Besides, the participants reported a need for a nuanced and multifocal approach to domestic violence prevention, including addressing the specificities of different vulnerable groups such as the elderly and the LGBQT+ community. They also talked about the importance of initiatives aimed at removing the stigma and taboo around domestic violence, also through targeting higher education establishments.

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