Relevant bibliographies by topics / Arimaa

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Arimaa'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Arimaa"

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Syed, Omar, and Jeroen Donkers. "THE ARIMAA MATCH." ICGA Journal 27, no. 2 (June 1, 2004): 109. http://dx.doi.org/10.3233/icg-2004-27208.

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Fotland, David. "Computer Arimaa: The Beginning." ICGA Journal 38, no. 1 (March 1, 2015): 12–18. http://dx.doi.org/10.3233/icg-2015-38103.

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Lewis, Andy. "Game Over, Arimaa?1." ICGA Journal 38, no. 1 (March 1, 2015): 55–62. http://dx.doi.org/10.3233/icg-2015-38108.

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Wu, David J. "Designing a Winning Arimaa Program." ICGA Journal 38, no. 1 (March 1, 2015): 19–40. http://dx.doi.org/10.3233/icg-2015-38104.

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Syed, Omar, and Jeroen Donkers. "The 2nd Arimaa Challenge Match." ICGA Journal 28, no. 2 (June 1, 2005): 118. http://dx.doi.org/10.3233/icg-2005-28212.

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Syed, Omar. "THE 5th ARIMAA CHALLENGE MATCH." ICGA Journal 31, no. 2 (June 1, 2008): 116. http://dx.doi.org/10.3233/icg-2008-31208.

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Syed, Omar. "The 7th Annual Arimaa Challenge Match." ICGA Journal 33, no. 2 (June 1, 2010): 108–9. http://dx.doi.org/10.3233/icg-2010-33206.

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Syed, Omar. "THE 5th ARIMAA COMPUTER WORLD CHAMPIONSHIP." ICGA Journal 31, no. 2 (June 1, 2008): 114–15. http://dx.doi.org/10.3233/icg-2008-31207.

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Syed, Omar. "The 6th Annual Arimaa Challenge Match." ICGA Journal 32, no. 2 (June 1, 2009): 119–20. http://dx.doi.org/10.3233/icg-2009-32221.

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Syed, Omar. "The Arimaa Challenge: From Inception to Completion." ICGA Journal 38, no. 1 (March 1, 2015): 3–11. http://dx.doi.org/10.3233/icg-2015-38102.

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Dissertations / Theses on the topic "Arimaa"

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Keisala, Simon. "Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188.

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Agents being able to play board games such as Tic Tac Toe, Chess, Go and Arimaa has been, and still is, a major difficulty in Artificial Intelligence. For the mentioned board games, there is a certain amount of legal moves a player can do in a specific board state. Tic Tac Toe have in average around 4-5 legal moves, with a total amount of 255168 possible games. Both Chess, Go and Arimaa have an increased amount of possible legal moves to do, and an almost infinite amount of possible games, making it impossible to have complete knowledge of the outcome. This thesis work have created various Neural Networks, with the purpose of evaluating the likelihood of winning a game given a certain board state. An improved evaluation function would compensate for the inability of doing a deeper tree search in Arimaa, and the anticipation is to compete on equal skills against another well-performing agent (meijin) having one less search depth. The results shows great potential. From a mere one hundred games against meijin, the network manages to separate good from bad positions, and after another one hundred games able to beat meijin with equal search depth. It seems promising that by improving the training and by testing different sizes for the neural network that a neural network could win even with one less search depth. The huge branching factor of Arimaa makes such an improvement of the evaluation beneficial, even if the evaluation would be 10 000 times more slow.
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Wahlström, Marco, and Jonas Karlsson. "Multi-Agent-System till brädspel." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2388.

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För att ta reda på hur väl en Multi-Agent-Systems-bot kan stå sig mot andra, icke-MAS-bottar, så har vi implementerat en bot till brädspelet Arimaa. Botten är implementerad i C++ och den kan spela mot andra bottar, eller människor, genom Arimaas officiella hemsida. Syftet har varit att skapa en fullfjädrad bot som både klarar av att spela korrekt, och att spela bra. För att ta reda på om MAS är en bra designfilosofi för Arimaa så har vi utmanat ett antal av de bottar som andra människor skapat och lagt upp på hemsidan. Alla bottarna har under tiden de legat uppe blivit rankade genom tävlingar och utmaningar och flera av dessa har tävlat om stora pengar, vilket betyder att människor har lagt mycket tid på dem. Efter ett stort antal matcher mot andra bottar så har vi kommit fram till att Arimaa är ett väldigt svårt spel att koda bottar till. Vi lyckades bara slå några av de sämsta bottarna på hemsidan men MAS visar stor potential och vi tror att man kan göra väldigt avancerade bottar med det. Jämfört med de bästa bottarna så är vår väldigt snabb och minneseffektiv. Man borde absolut experimentera mer.
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Uppling, Hugo, and Adam Eriksson. "Single and multiple step forecasting of solar power production: applying and evaluating potential models." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.

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The aim of this thesis is to apply and evaluate potential forecasting models for solar power production, based on data from a photovoltaic facility in Sala, Sweden. The thesis evaluates single step forecasting models as well as multiple step forecasting models, where the three compared models for single step forecasting are persistence, autoregressive integrated moving average (ARIMA) and ARIMAX. ARIMAX is an ARIMA model that also takes exogenous predictors in consideration. In this thesis the evaluated exogenous predictor is wind speed. The two compared multiple step models are multiple step persistence and the Gaussian process (GP). Root mean squared error (RMSE) is used as the measurement of evaluation and thus determining the accuracy of the models. Results show that the ARIMAX models performed most accurate in every simulation of the single step models implementation, which implies that adding the exogenous predictor wind speed increases the accuracy. However, the accuracy only increased by 0.04% at most, which is determined as a minimal amount. Moreover, the results show that the GP model was 3% more accurate than the multiple step persistence; however, the GP model could be further developed by adding more training data or exogenous variables to the model.
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Kučírek, Tomáš. "Umělá inteligence pro hraní her." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412861.

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Arimaa is a strategic board game for two players. It was designed to be simple for human players and difficult for computers. The aim of this thesis is to design and implement the program with features of the artificial intelligence, which would be able to defeat human players. The implementation was realized in the three key parts: evaluation position, generation of moves and search. The program was run on the game server and defeated many bots as well as human players.
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Ribeiro, Liliana Patrícia Teixeira. "Aplicação de modelos econométricos na previsão de preço de azeites." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20862.

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Mestrado em Econometria Aplicada e Previsão
O presente relatório tem por base as atividades desenvolvidas no estágio na empresa Gallo Worldwide, nomeadamente a análise das bases de dados da empresa de modo a efetuar a previsão do preço do azeite extra-virgem, azeite virgem e lampante. Uma vez que a modelação dos preços dos azeites é realizada através da modelação de séries temporais, existem diversos modelos que podem ser aplicados. Segundo a literatura científica analisada, a estimação das séries temporais utilizadas pode ser realizada através do modelo ARIMA, ARIMAX, GARCH e SUR. Neste sentido, será apresenta de uma forma detalhada a análise dos modelos econométricos em estudo para a obtenção das previsões pretendidas. Os modelos utilizados foram aplicados a conjuntos de dados com diferentes periodicidades: semanal e mensal. Sendo os modelos aplicados a conjuntos de dados com diferentes periodicidades também foram efetuadas previsões através de todos os modelos aplicados aos dois conjuntos de dados, existindo conclusões para ambos os casos.
The current report was built around the tasks performed during the internship on the company Gallo Worldwide, where the main responsibilities consisted in the analysis of the database to be able to forecast extra-virgin olive oil, virgin olive oil and lampante prices. Considering the olive oil pricing modelling is achieved through the modelling of time series, several models can be applied. According to the scientific literature reviewed, the estimation of time series may be accomplished using the ARIMA, ARIMAX, GARCH and SUR models. In this sense, it will be presented, in a detailed manner, the analysis of the econometrical models being studied as a resource to obtain the intended predictions. The models utilized were applied to a group of data with different periodicities: data with weekly periodicity and data with monthly periodicity. Considering the models are employed over a set of data with different periodicities, similarly the predictions were made through all the models used in both sets of data, resulting in the existence ofconclusions for both cases.
info:eu-repo/semantics/publishedVersion
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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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Fredén, Daniel, and Hampus Larsson. "Forecasting Daily Supermarkets Sales with Machine Learning." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276483.

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Improved sales forecasts for individual products in retail stores can have a positive effect both environmentally and economically. Historically these forecasts have been done through a combination of statistical measurements and experience. However, with the increased computational power available in modern computers, there has been an interest in applying machine learning for this problem. The aim of this thesis was to utilize two years of sales data, yearly calendar events, and weather data to investigate which machine learning method could forecast sales the best. The investigated methods were XGBoost, ARIMAX, LSTM, and Facebook Prophet. Overall the XGBoost and LSTM models performed the best and had a lower mean absolute value and symmetric mean percentage absolute error compared to the other models. However, Facebook Prophet performed the best in regards to root mean squared error and mean absolute error during the holiday season, indicating that Facebook Prophet was the best model for the holidays. The LSTM model could however quickly adapt during the holiday season improved the performance. Furthermore, the inclusion of weather did not improve the models significantly, and in some cases, the results were worsened. Thus, the results are inconclusive but indicate that the best model is dependent on the time period and goal of the forecast.
Förbättrade försäljningsprognoser för individuella produkter inom detaljhandeln kan leda till både en miljömässig och ekonomisk förbättring. Historiskt sett har dessa utförts genom en kombination av statistiska metoder och erfarenhet. Med den ökade beräkningskraften hos dagens datorer har intresset för att applicera maskininlärning på dessa problem ökat. Målet med detta examensarbete är därför att undersöka vilken maskininlärningsmetod som kunde prognostisera försäljning bäst. De undersökta metoderna var XGBoost, ARIMAX, LSTM och Facebook Prophet. Generellt presterade XGBoost och LSTM modellerna bäst då dem hade ett lägre mean absolute value och symmetric mean percentage absolute error jämfört med de andra modellerna. Dock, gällande root mean squared error hade Facebook Prophet bättre resultat under högtider, vilket indikerade att Facebook Prophet var den bäst lämpade modellen för att förutspå försäljningen under högtider. Dock, kunde LSTM modellen snabbt anpassa sig och förbättrade estimeringarna. Inkluderingen av väderdata i modellerna resulterade inte i några markanta förbättringar och gav i vissa fall även försämringar. Övergripande, var resultaten tvetydiga men indikerar att den bästa modellen är beroende av prognosens tidsperiod och mål.
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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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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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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.
Made available in DSpace on 2012-10-20T11:18:26Z (GMT). No. of bitstreams: 1 196113.pdf: 2449450 bytes, checksum: 56b01ccb27ccad38aa728c00dc2045ee (MD5)
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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Books on the topic "Arimaa"

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Juhnke, Fritz. Beginning arimaa: Chess reborn beyond computer comprehension. [Garland, Tex.?]: Flying Camel Publications, 2009.

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Malinin, Evgeniǐ. Prokli︠a︡tie Arimana. Moskva: AST, 2001.

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Malinin, Evgeniǐ. Prokli︠a︡tie Arimana. Moskva: AST, 2000.

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

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

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Mori Arimasa no nikki. Tōkyō: Shinchi Shobō, 1986.

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Prokli͡atie Arimana: Razdelennyĭ mir. Moskva: Izd-vo AST, 2001.

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Mori Arimasa no nikki. Tōkyō: Shinchi Shobō, 1987.

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Sako, Jun'ichirō. Mori Arimasa no nichiki. Tōkyō: Shinchi Shobō, 1986.

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Thomson, Duncan. Arikha. London: Phaidon Press, 1994.

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Book chapters on the topic "Arimaa"

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Fotland, David. "Building a World-Champion Arimaa Program." In Computers and Games, 175–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11674399_12.

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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 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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Shumway, Robert H., and David S. Stoffer. "ARIMA Models." In Springer Texts in Statistics, 83–171. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7865-3_3.

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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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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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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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Conference papers on the topic "Arimaa"

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Vega, Jose Roberto Mercado, and Zvi Retchkiman Konigsberg. "Modeling the game of Arimaa with Linguistic Geometry." In 2009 IEEE Symposium on Computational Intelligence and Games (CIG). IEEE, 2009. http://dx.doi.org/10.1109/cig.2009.5286450.

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Rubio, Jose Miguel, and Juan Salas. "Building an Arimaa-Playing Program Using Basic Search Algorithms." In 2017 International Conference on Information Systems and Computer Science (INCISCOS). IEEE, 2017. http://dx.doi.org/10.1109/inciscos.2017.44.

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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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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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Ishihara, M., T. Otsuka, T. Mizusaki, and K. Yazaki. "FRONTIERS OF NUCLEAR STRUCTURE PHYSICS." In Proceedings of the International Symposium held in Honor of Akito Arima. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789814531467.

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Wagner, Shannon M., and John B. Ferris. "Reduced Order ARIMA Models of 2-D Terrain Profiles Using Singular Value Decomposition." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43388.

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The terrain profile is the principal source of vertical excitation to a vehicle’s chassis. To correctly predict the system response an accurate model of the terrain is needed. It is impractical to simulate long data sets; therefore it is necessary to characterize roads so that they can be grouped into sets with similar physical characteristics. The first step is to consider the road to be a realization of an underlying stochastic process. This work develops a method for characterizing non-stationary terrain profiles though ARIMA (autoregressive integrated moving average) modeling and singular value decomposition techniques. It is proposed that the ARIMA coefficients and the distribution of the residual process are jointly dependent functions of the physical characteristics of road profiles. This dependence is then exploited by mapping these dependent functions onto a smaller set of random variables. The resulting number of coefficients required to characterize the terrain is greatly reduced. Examples demonstrate that non-stationary road profiles can be characterized in this manner. Future work in polynomial chaos and ARIMA modeling are discussed within the context of terrain characterization.
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Almeida, Charles M. P., Geymerson S. Ramos, and Andre L. L. Aquino. "Predição de dados de sensoriamento visando eficiência energética de redes de sensores sem fio." In X Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcup.2018.3282.

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Este artigo tem como objetivo comparar modelos de predição de dados de sensoriamento em redes de sensores sem fio com a finalidade de economizar energia na coleta de dados. Modelos ARIMA, SVM e ANN foram utilizados em uma aplicação de coleta de dados de temperatura e avaliados quanto à economia de energia proporcionada. As medições foram realizados por dispositivos reais e foi possível observar o desempenho dos modelos para um conjunto de dados de sensoriamento em um ambiente de escritório. Para este estudo de caso, o modelo ARIMA apresentou melhor desempenho em relação ao SVM e ANN em termos de eficiência energética.
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Huotari, Matti, Shashank Arora, Avleen Malhi, and Kary Främling. "A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23949.

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Abstract It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery.
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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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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 "Arimaa"

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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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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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Alley, Patricia. Request to Register with LANL in Ariba www.lanl.gov/business. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1804328.

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

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Lee, J., J. Lee, J. Kim, D. Kwon, and C. Kim. A Description of the ARIA Encryption Algorithm. RFC Editor, March 2010. http://dx.doi.org/10.17487/rfc5794.

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