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Academic literature on the topic 'ARIMA'

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Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "ARIMA"

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Panjaitan, Helmi, Alan Prahutama, and Sudarno Sudarno. "PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)." Jurnal Gaussian 7, no. 1 (February 28, 2018): 96–109. http://dx.doi.org/10.14710/j.gauss.v7i1.26639.

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Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting
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ALKALI, MUSA ABUBAKAR. "ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA." Asia Proceedings of Social Sciences 4, no. 1 (April 17, 2019): 4–6. http://dx.doi.org/10.31580/apss.v4i1.528.

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This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.
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Bielak, Jarosław. "Prognozowanie rynku pracy woj. lubelskiego z wykorzystaniem modeli ARIMA i ARIMAX." Barometr Regionalny. Analizy i Prognozy, no. 1 (19) (May 13, 2010): 27–44. http://dx.doi.org/10.56583/br.1379.

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W artykule przedstawiono metodę prognozowania rynku pracy – poziomu bezrobocia i przeciętnego zatrudnienia – w woj. lubelskim w oparciu o modele ARIMA i ARIMAX. Dodatkowymi zmiennymi egzogenicznymi wprowadzanymi do standardowych modeli ARIMA były szeregi wartości indeksu nastrojów gospodarczych. Pokazano różnice we wskaźnikach charakteryzujących jakość prognoz generowanych przez model ARIMAX i „czysty” model ARIMA. Uwzględniono modele budowane dla danych kwartalnych i dla danych miesięcznych oraz omówiono sposób konwersji kwartalnych szeregów czasowych indeksu nastrojów gospodarczych do szeregów miesięcznych. Wykonano analizę weryfikującą rzeczywistą przydatność takiej metody prognozowania i korzyści płynące z jej stosowania.
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Grillenzoni, Carlo. "ARIMA Processes with ARIMA Parameters." Journal of Business & Economic Statistics 11, no. 2 (April 1993): 235. http://dx.doi.org/10.2307/1391375.

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Grillenzoni, Carlo. "ARIMA Processes With ARIMA Parameters." Journal of Business & Economic Statistics 11, no. 2 (April 1993): 235–50. http://dx.doi.org/10.1080/07350015.1993.10509952.

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Amelia, R., D. Y. Dalimunthe, E. Kustiawan, and I. Sulistiana. "ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia." IOP Conference Series: Earth and Environmental Science 926, no. 1 (November 1, 2021): 012034. http://dx.doi.org/10.1088/1755-1315/926/1/012034.

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Abstract In recent years, the weather and climate are unpredictable and the most visible is the rotation of the rainy season and the dry season. The extreme changes in rainfall can cause disasters and losses for the community. For that we need to predict the rainfall to anticipate the worst events. Rainfall is included in the periodic series data, so the forecasting method that can be used is the ARIMAX model which is ARIMA model expanded by adding the exogen variable. The aim of this research is to predict the rainfall data in Pangkalpinang City, Indonesia. The best model for each rainfall is ARIMAX (0,1,3) for monthly rainfall data and ARIMAX (0,1,2) for maximum daily rainfall. This research shows that there is an influence maximum wind speed variable to monthly rainfall and maximum daily rainfall in the Pangkalpinang City. Nevertheless, when viewed from the ARIMA and ARIMAX models based on the obtained AIC value, the ARIMAX value is still better than ARIMA. However, the prediction value using ARIMAX needs to increase again to take into account seasonal data rainfall. Then, possible to add other exogeneous factors besides maximum wind speed.
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Diksa, I. Gusti Bagus Ngurah. "Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (January 1, 2022): 237–50. http://dx.doi.org/10.20956/j.v18i2.18542.

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Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).
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TAMUKE, Edmund, Emerson Abraham JACKSON, and Abdulai SILLAH. "FORECASTING INFLATION IN SIERRA LEONE USING ARIMA AND ARIMAX: A COMPARATIVE EVALUATION. MODEL BUILDING AND ANALYSIS TEAM." Theoretical and Practical Research in the Economic Fields 9, no. 1 (June 30, 2018): 63. http://dx.doi.org/10.14505/tpref.v9.1(17).07.

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The study has provided empirical investigation of both ARIMA and ARIMAX methodology as a way of providing forecast of Headline Consumer Price Index (HCPI) for Sierra Leone based on data collected from the Sierra Leone Statistical Office and the Bank of Sierra Leone. In this, the main research question of addressing outcomes from in and out-of-sample forecast were provided using the Static technique and this shows that both methodologies were proved to have tracked past and future occurrences of HCPI with minimal margin of error as indicated in the MAPE results. In a similar note, the key objective of identifying whether the ARIMAX methodology or the ARIMA methodology is a better predictor of forecasting future trends in HCPI. However, on the whole, both ARIMA and ARIMAX seem to have provided very good outcome in predicting future events of HCPI, particularly when Static technique is used as the option for forecasting outcomes, with the ARIMAX marginally coming out as the preferred choice on the basis of its evaluation outcomes.
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ISMAIL, NUR AFIQAH, NURIN ALYA RAMZI, and Pauline Jin Wee Mah. "FORECASTING THE UNEMPLOYMENT RATE IN MALAYSIA DURING COVID-19 PANDEMIC USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 7, no. 1 (February 28, 2022): 982. http://dx.doi.org/10.24191/mjoc.v7i1.14641.

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The unemployment issue is one of the most common problems faced by many countries around the world. The unemployment rates in developed countries often fluctuate throughout time. Similarly, Malaysia is also affected by the inconsistent unemployment rate especially during the COVID-19 pandemic. Therefore, in order to understand the trend better, ARIMA and ARFIMA were used to model and forecast the unemployment rate in Malaysia in this study. The dataset on the unemployment rate in Malaysia from January 2010 until July 2021 was obtained from Bank Negara Malaysia (BNM) official portal. The best time series models found were ARIMA (2, 1, 2) and ARFIMA (0, −0.2339, 0). The performance of the models was evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). It appeared that the ARFIMA model emerged as a better forecast model since it had better performance compared to ARIMA in forecasting the unemployment rate in Malaysia.
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Chen, Yun-Peng, Le-Fan Liu, Yang Che, Jing Huang, Guo-Xing Li, Guo-Xin Sang, Zhi-Qiang Xuan, and Tian-Feng He. "Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China." International Journal of Environmental Research and Public Health 19, no. 9 (April 28, 2022): 5385. http://dx.doi.org/10.3390/ijerph19095385.

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The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.
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Dissertations / Theses on the topic "ARIMA"

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Abalos, Choque Melisa. "Modelo Arima con intervenciones." Universidad Mayor de San Andrés. Programa Cybertesis BOLIVIA, 2009. http://www.cybertesis.umsa.bo:8080/umsa/2009/abalos_cme/html/index-frames.html.

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El desarrollo de gran parte de los modelos y métodos estadísticos, específicamente relacionados con series temporales, ha ido ligado al deseo de estudiar aplicaciones específicas dentro de diversos ámbitos científicos. El presente trabajo también surgió con el objetivo de resolver diversos problemas que se plantean dentro del ámbito econométrico, aunque también puede ser usado en otros ámbitos, todos ellos ligados con un conjunto de datos históricos y con una aplicación muy concreta al estudio del “egreso de divisas” en Bolivia. Se han estudiado a profundidad los modelos para series temporales que únicamente dependían del pasado de la propia serie. En el presente trabajo se inicia el análisis de una serie temporal teniendo en cuenta algún tipo de información externa. En el capítulo 1 se sustenta fuertemente el hecho de investigar acerca de aspectos ajenos a la serie temporal que llegan de algún modo a alterar su normal comportamiento. El capítulo 2 desarrolla minuciosamente modelos univariantes conocidos con el nombre de ARIMA, desarrollando su parte teórica. Posteriormente se complementa esta perspectiva univariante añadiéndose una parte determinística correspondiente al análisis de intervención construyendo así el modelo ARIMA CON INTERVENCIONES, la utilización de éstos modelos es comparada en el capítulo 3, de esta manera se distingui cual de los dos es más efectivo cuando los datos son afectados por eventos circunstanciales. La metodología del modelo ARIMA CON INTERVENCIONES es una herramienta útil para “modelizar” el comportamiento de las series temporales que presentan modificaciones a raíz de eventos ajenos que no pueden ser controlados.
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Rostami, Tabar Bahman. "ARIMA demand forecasting by aggregation." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00980614.

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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce demand uncertainty and consequently improve the forecasting (and inventory control) performance. An intuitively appealing such approach that is known to be effective is demand aggregation. One approach is to aggregate demand in lower-frequency 'time buckets'. Such an approach is often referred to, in the academic literature, as temporal aggregation. Another approach discussed in the literature is that associated with cross-sectional aggregation, which involves aggregating different time series to obtain higher level forecasts.This research discusses whether it is appropriate to use the original (not aggregated) data to generate a forecast or one should rather aggregate data first and then generate a forecast. This Ph.D. thesis reveals the conditions under which each approach leads to a superior performance as judged based on forecast accuracy. Throughout this work, it is assumed that the underlying structure of the demand time series follows an AutoRegressive Integrated Moving Average (ARIMA) process.In the first part of our1 research, the effect of temporal aggregation on demand forecasting is analysed. It is assumed that the non-aggregate demand follows an autoregressive moving average process of order one, ARMA(1,1). Additionally, the associated special cases of a first-order autoregressive process, AR(1) and a moving average process of order one, MA(1) are also considered, and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregate and the non-aggregate demand in order to contrast the relevant forecasting performances. The theoretical analysis is validated by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant value used for SES and the process parameters.In the second part of our research, the effect of cross-sectional aggregation on demand forecasting is evaluated. More specifically, the relative effectiveness of top-down (TD) and bottom-up (BU) approaches are compared for forecasting the aggregate and sub-aggregate demands. It is assumed that that the sub-aggregate demand follows either a ARMA(1,1) or a non-stationary Integrated Moving Average process of order one, IMA(1,1) and a SES procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and, as discussed above, SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA(1) process). Theoretical Mean Squared Errors are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate levels in addition to empirically validating our findings on a real dataset from a European superstore. The results show that the superiority of each approach is a function of the series autocorrelation, the cross-correlation between series and the comparison level.Finally, for both parts of the research, valuable insights are offered to practitioners and an agenda for further research in this area is provided.
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Mariotti, Mara Terezinha. "Análise arima de dados meteo-oceanográficos." Florianópolis, SC, 2003. http://repositorio.ufsc.br/xmlui/handle/123456789/84655.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Ambiental.
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Um estudo do mecanismo gerador das componentes meteorológicas que afetam o nível do mar é proposto através da utilização de modelos ARIMA (autorregressive integrated moving average). Séries temporais da temperatura do ar, pressão atmosférica, da componente meridional do vento e do nível do mar foram aquisitadas em São Francisco do Sul-SC, no período de 14 de julho a 15 de dezembro de 1996, e reamostradas a cada seis horas para melhor avaliar as componentes de baixa freqüência. As séries se mostraram não estacionárias na média, impondo a necessidade de integração. Não foi possível identificar uma não estacionaridade da variância devido ao comprimento insuficiente dos registros utilizados. Nos modelos de ordem 2 a estrutura de recorrência entre dois sistemas frontais é reconhecida através do modo associado aos dois pólos do polinômio. Os modelos AR(4) de todas as variáveis consideradas conseguem reconstruir também a evolução do sistema in situ, de período aproximado de 2,5 dias, por meio da segunda dupla de pólos. Modelos autorregressivos de ordem superior poderiam melhorar a identificação e a reconstrução desses ciclos, mas não conseguem convergir devido a não estacionaridade. Apesar disso, modelos de baixa ordem, com dois parâmetros apenas, conseguem fazer previsões aceitáveis até 24 horas, o que demonstra as possibilidades da metodologia.
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Örneholm, Filip. "Anomaly Detection in Seasonal ARIMA Models." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388503.

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Vollenbröker, Bernd Karl [Verfasser], and Alexander [Akademischer Betreuer] Lindner. "Strictly Stationary Solutions of Multivariate ARMA and Univariate ARIMA Equations / Bernd Karl Vollenbröker ; Betreuer: Alexander Lindner." Braunschweig : Technische Universität Braunschweig, 2011. http://d-nb.info/1175824860/34.

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Guimarães, Rita Cabral Pereira de Castro. "Modelização ARIMA de sucessões cronológicas: aplicação na previsão de escoamentos mensais." Master's thesis, Universidade de Évora, 1997. http://hdl.handle.net/10174/13282.

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Os modelos ARIMA tem vindo a ser cada vez mais utilizados na modelização e previsão de sucessões hidrológicas, instrumento fundamental para o planeamento e gestão de qualquer sistema do domínio Hídrico. A modelização de tais sucessões é conseguida através de uma metodologia em três etapas, desenvolvida por G. E. P. Box e G. M. Jenkins. Deste processo resulta um modelo, considerado como o mais adequado para representar a sucessão, podendo este ser então utilizado na previsão de eventos futuros. Para a aplicação destes modelos utilizaram-se seis sucessões de escoamentos mensais observados em três cursos de água pertencentes à bacia hidrográfica do Rio Douro. A modelização efectuada para esta sucessões permitiu eleger, para cada uma delas, um modelo ARIMA, com o qual se estabeleceram previsões para dois anos consecutivos à última observação. / Abstract - ARIMA models have become an important tool for modelling and forecasting of hydrologic sequences. Theses techniques are of considerable importance to the design and operation of water resource systems. Before being able to forecasting future values, models have to be found which describe past data adequately. These is accomplished with a iterative process, developed by G. E. P. Box and G. M. Jenkins, which incorporates three stages. For the applications of these models we selected six monthly flow sequences for three rivers located in Douro River watershed. The modelling of such sequences gave one ARIMA model for the forecasting of flows two years ahead.
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Філатова, Ганна Петрівна, Анна Петровна Филатова, and Hanna Petrivna Filatova. "Прогнозування державного боргу з використанням ARIMA моделі." Thesis, ЦФЕНД, 2020. https://essuir.sumdu.edu.ua/handle/123456789/84293.

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Державний борг як важливий фактор соціально-економічного розвитку держави виступає свого роду індикатором і критерієм ефективності провадження виваженої боргової політики держави, а його прогнозування займає одне з ключових місць в процесі забезпечення економічної безпеки держави. У сучасній статистичній теорії існує безліч різноманітних методів прогнозування економічної інформації. Значна їх частина стосується прогнозування часових рядів, без додаткової інформації, тобто без аналізу впливу інших факторів. Звичайно, такий аналіз є доволі неповним, але досить часто результати таких прогнозів є більш точними порівняно з іншими методами прогнозування. Одним з таких методів є побудова ARIMA моделі.
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Muller, Daniela. "Estimação para os parâmetros de processos estocásticos estacionários com característica de longa dependência." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1999. http://hdl.handle.net/10183/127017.

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Estudos recentes em séries temporais direcionam-se àquelas que apresentam característica de longa dependência, ou seja, séries temporais nas quais a dependência entre observações distantes não é desprezível. Neste trabalho, analisamos o modelo ARFIN!A(p, d,q ), para dE (0,0;0,5), que apresenta a. característica de longa dependência. Como estimativas para o grau de diferenciação d consideramos os estimadores obtidos através da função periodograma, da função periodograma suavizado e da função de máxima verossimilhança sugerida por Whittle, comparando a variância e o erro quadrático médio destes estimadores através de diversas simulações.
Recent work on time series analysis is concerned with the property of long mcmory, that is, time series in which the dependence between distant observations is not negligible. In this work we analyzc the ARF I .NI A(p, d, q) model, for d E (0.0; 0.5), that has the property of long memory. We consider estimators for the degree of differencing d based on the perioclogram function, on the smoothed periodogram function , anel on the maximum likelihood function suggested by Whittle. Through several simulations we compare the variance anel the mean squared error for these estimators.
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Isbister, Tim. "Anomaly detection on social media using ARIMA models." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269189.

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This thesis explores whether it is possible to capture communication patterns from web-forums and detect anomalous user behaviour. Data from individuals on web-forums can be downloaded using web-crawlers, and tools as LIWC can make the data meaningful. If user data can be distinguished from white noise, statistical models such as ARIMA can be parametrized to identify the underlying structure and forecast data. It turned out that if enough data is captured, ARIMA models could suggest underlying patterns, therefore anomalous data can be identified. The anomalous data might suggest a change in the users' behaviour.
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Cardoso, Neto Jose. "Agregação temporal de variavel fluxo em modelos Arima." [s.n.], 1990. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305854.

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Orientador : Luiz Koodi Hotta
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Ciencia da Computação
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Mestrado
Mestre em Estatística
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Books on the topic "ARIMA"

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Arima Akito. Tōkyō: Kashinsha, 2002.

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Arima, Yoriyasu. Arima Yoriyasu nikki. Tōkyō: Shōyū Kurabu, 1997.

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Arima, Takashi. Arima Takashi shishū. Tōkyō: Doyō Bijutsusha, 1988.

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Tanaka, Mojirō. Hyōden Arima Takashi. Tōkyō: Doyō Bijutsusha Shuppan Hanbai, 2011.

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Iinkai, Shibukawa-shi Kyōiku. Arima kugūmado iseki. Shibukawa: Shibukawa-shi Kyōiku Iinkai, 1997.

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Arima Ineko "waga ai". Tōkyō: Kōdansha, 1985.

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Gunma-ken Maizō Bunkazai Chōsa Jigyōdan, ed. Arima iseki: Yayoi, Kofun Jidai. Kitatachibana-mura (Gunma-ken): Gunma-ken kōko shiryō fukyūkai, 1990.

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Arima Akito kushū, fuki: Fuki. Tōkyō: Kadokawa Shoten, 2004.

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Meyler, Aidan. Forecasting Irish inflation using ARIMA models. Dublin: Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.

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Iinkai, Shibukawa-shi Kyōiku. Arima haiji ato hakkutsu chōsa gaihō. Shibukawa: Shibukawa-shi Kyōiku Iinkai, 1987.

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Book chapters on the topic "ARIMA"

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Schips, Bernd. "ARMA- und ARIMA-Modelle." In Beiträge zur psychologischen Forschung, 281–84. Wiesbaden: Gabler Verlag, 1990. http://dx.doi.org/10.1007/978-3-322-89329-1_38.

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Gass, Saul I., and Carl M. Harris. "ARIMA." In Encyclopedia of Operations Research and Management Science, 1. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_5.

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Shumway, Robert H., and David S. Stoffer. "ARIMA Models." In Springer Texts in Statistics, 75–163. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52452-8_3.

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Groß, Jürgen. "ARIMA Modelle." In Grundlegende Statistik mit R, 251–60. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9677-3_24.

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Franke, Jürgen, Wolfgang Härdle, and Christian Hafner. "ARIMA Zeitreihenmodelle." In Einführung in die Statistik der Finanzmärkte, 177–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17049-2_11.

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Harvey, A. C. "ARIMA Models." In Time Series and Statistics, 22–24. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_2.

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Shumway, Robert H., and David S. Stoffer. "ARIMA Models." In Time Series: A Data Analysis Approach Using R, 99–128. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429273285-5.

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Harvey, A. C. "Arima Models." In The New Palgrave Dictionary of Economics, 414–16. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_533.

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Franke, Jürgen, Wolfgang Härdle, and Christian Hafner. "ARIMA Zeitreihenmodelle." In Einführung in die Statistik der Finanzmärkte, 179–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-97127-3_11.

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Harvey, A. C. "Arima Models." In The New Palgrave Dictionary of Economics, 1–3. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_533-1.

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Conference papers on the topic "ARIMA"

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Liu, Kai, Xi Zhang, and YangQuan Chen. "An Evaluation of ARFIMA Programs." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67483.

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Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA model. Therefore, it has much wider applications since it could capture both short-range dependence and long range dependence. For now, several software have developed functions dealing with ARFIMA processes. However, it could be a big difference, if using different numerical tools for time series analysis. Time to time, being asked about which tool is suitable for a specific application, the authors decide to carry out this survey to present recapitulative information of the available tools in the literature, in hope of benefiting researchers with different academic backgrounds. In this paper, 4 primary functions concerning simulation, fractional order difference filter, estimation and forecast are compared and evaluated respectively in the different software and informative comments are also provided for selection.
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Yan, Zhixian. "Traj-ARIMA." In the Second International Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1899441.1899446.

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Xu, Yuqing, Guangxia Xu, Zeliang An, and Yanbin Liu. "EPSTO-ARIMA: Electric Power Stochastic Optimization Predicting Based on ARIMA." In 2021 IEEE 9th International Conference on Smart City and Informatization (iSCI). IEEE, 2021. http://dx.doi.org/10.1109/isci53438.2021.00019.

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Colak, Ilhami, Mehmet Yesilbudak, Naci Genc, and Ramazan Bayindir. "Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.33.

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Gupta, Akshita, and Arun Kumar. "Mid Term Daily Load Forecasting using ARIMA, Wavelet-ARIMA and Machine Learning." In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2020. http://dx.doi.org/10.1109/eeeic/icpseurope49358.2020.9160563.

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Barbulescu, Alina, and Cristian Stefan Dumitriu. "ARIMA and Wavelet-ARIMA Models for the Signal Produced by Ultrasound in Diesel." In 2021 25th International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2021. http://dx.doi.org/10.1109/icstcc52150.2021.9607321.

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Hemanth Kumar P. and S. Basavaraj Patil. "Estimation & forecasting of volatility using ARIMA, ARFIMA and Neural Network based techniques." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154853.

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Aji, Bimo Satrio, Indwiarti, and Aniq Atiqi Rohmawati. "Forecasting Number of COVID-19 Cases in Indonesia with ARIMA and ARIMAX Models." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527453.

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Ruan, Li, Yongji Wang, Qing Wang, Fengdi Shu, Haitao Zeng, and Shen Zhang. "ARIMAmmse: An Improved ARIMA-based." In 30th Annual International Computer Software and Applications Conference. IEEE, 2006. http://dx.doi.org/10.1109/compsac.2006.115.

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Yaacob, Asrul H., Ian K. T. Tan, Su Fong Chien, and Hon Khi Tan. "ARIMA Based Network Anomaly Detection." In 2010 Second International Conference on Communication Software and Networks. IEEE, 2010. http://dx.doi.org/10.1109/iccsn.2010.55.

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Reports on the topic "ARIMA"

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Cook, Steve. Visual identification of ARIMA models. Bristol, UK: The Economics Network, January 2016. http://dx.doi.org/10.53593/n2817a.

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Chang, J. L., H. Nazari, C. O. Font, G. C. Gilbreath, and E. Oh. Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada472169.

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Hafer, R. W., Scott E. Hein, and Clemens J. M. Kool. Comparing Multi-State Kalman Filter and ARIMA Forecasts: An Application to the Money Multiplier. Federal Reserve Bank of St. Louis, 1985. http://dx.doi.org/10.20955/wp.1985.001.

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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, June 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards.
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Pina-Burón, María Rosa. Cerro de Ariza. Institut Català d’Arqueologia Clàssica, 2022. http://dx.doi.org/10.51417/figlinae_003.

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Ficha del yacimiento Cerro de Ariza ubicado a Écija (Córdoba) incluida en el proyecto "Figlinae Hispanae (FIGHISP). Catálogo en red de las alfarerías hispanorromanas y estudio de la comercialización de sus productos".
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Hopkins, Matthew Morgan, Harry K. Moffat, David R. Noble, Patrick K. Notz, and Samuel Ramirez Subia. Aria 1.5 : user manual. Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/922079.

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Ekdahl, Carl August Jr. Beam Dynamics for ARIA. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1158826.

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Schulze, Martin E. ARIA Cell Solenoid Design Considerations. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1182616.

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Carnes, Brian R. Sierra/Aria 4.48 Verification Manual. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1433783.

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Carnes, Brian R. Sierra/Aria 4.56 Verification Manual. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1615879.

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