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Artykuły w czasopismach na temat "Neural time series"
Rudenko, Oleg, Oleksandr Bezsonov i Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron". Development Management 17, nr 1 (7.05.2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.
Pełny tekst źródłaGolovenko, A. O., i A. A. Kopyrkin. "Neural Network Forecasting of Time Series". Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 19, nr 4 (2019): 124–31. http://dx.doi.org/10.14529/ctcr190412.
Pełny tekst źródłaKolarik, Thomas, i Gottfried Rudorfer. "Time series forecasting using neural networks". ACM SIGAPL APL Quote Quad 25, nr 1 (październik 1994): 86–94. http://dx.doi.org/10.1145/190468.190290.
Pełny tekst źródłaXinhui, Wen, i Chen Kaizhou. "Time series neural network forecasting methods". Journal of Electronics (China) 12, nr 1 (styczeń 1995): 1–8. http://dx.doi.org/10.1007/bf02684561.
Pełny tekst źródłaLiu, Zhi Cheng. "Real Time Prediction Method of Sensor Output Time Series". Advanced Materials Research 912-914 (kwiecień 2014): 1322–26. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1322.
Pełny tekst źródłaPanigrahi, Sibarama, Yasobanta Karali i H. S. Behera. "Time Series Forecasting using Evolutionary Neural Network". International Journal of Computer Applications 75, nr 10 (23.08.2013): 13–17. http://dx.doi.org/10.5120/13146-0553.
Pełny tekst źródłaKim, JongHwa, Jong Hoo Choi i Changwan Kang. "Time Series Prediction Using Recurrent Neural Network". Korean Data Analysis Society 21, nr 4 (31.08.2019): 1771–79. http://dx.doi.org/10.37727/jkdas.2019.21.4.1771.
Pełny tekst źródłaZhang, Dongqing, i Yubing Han. "Time Series Prediction with RBF Neural Networks". Information Technology Journal 12, nr 14 (1.07.2013): 2815–19. http://dx.doi.org/10.3923/itj.2013.2815.2819.
Pełny tekst źródłaSako, Kady, Berthine Nyunga Mpinda i Paulo Canas Rodrigues. "Neural Networks for Financial Time Series Forecasting". Entropy 24, nr 5 (7.05.2022): 657. http://dx.doi.org/10.3390/e24050657.
Pełny tekst źródłaPérez-Chavarría, M. A. "Time series prediction using artificial neural networks". Ciencias Marinas 28, nr 1 (1.02.2002): 67–77. http://dx.doi.org/10.7773/cm.v28i1.205.
Pełny tekst źródłaRozprawy doktorskie na temat "Neural time series"
Kajitani, Yoshio. "Forecasting time series with neural nets". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ39836.pdf.
Pełny tekst źródłaAndreux, Mathieu. "Foveal autoregressive neural time-series modeling". Electronic Thesis or Diss., Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE073.
Pełny tekst źródłaThis dissertation studies unsupervised time-series modelling. We first focus on the problem of linearly predicting future values of a time-series under the assumption of long-range dependencies, which requires to take into account a large past. We introduce a family of causal and foveal wavelets which project past values on a subspace which is adapted to the problem, thereby reducing the variance of the associated estimators. We then investigate under which conditions non-linear predictors exhibit better performances than linear ones. Time-series which admit a sparse time-frequency representation, such as audio ones, satisfy those requirements, and we propose a prediction algorithm using such a representation. The last problem we tackle is audio time-series synthesis. We propose a new generation method relying on a deep convolutional neural network, with an encoder-decoder architecture, which allows to synthesize new realistic signals. Contrary to state-of-the-art methods, we explicitly use time-frequency properties of sounds to define an encoder with the scattering transform, while the decoder is trained to solve an inverse problem in an adapted metric
Bonato, Tommaso. "Time Series Predictions With Recurrent Neural Networks". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Znajdź pełny tekst źródłaBrax, Christoffer. "Recurrent neural networks for time-series prediction". Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-480.
Pełny tekst źródłaRecurrent neural networks have been used for time-series prediction with good results. In this dissertation recurrent neural networks are compared with time-delayed feed forward networks, feed forward networks and linear regression models on a prediction task. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns. It is evaluated if more accurate predictions can be made when only using the campaign segments of the data.
Throughout the entire project a knowledge discovery process, identified in the literature has been used to give a structured work-process. The results show that the recurrent network is not better than the other evaluated algorithms, in fact, the time-delayed feed forward neural network showed to give the best predictions. The results also show that more accurate predictions could be made when only using information from campaign segments.
ABELEM, ANTONIO JORGE GOMES. "ARTIFICIAL NEURAL NETWORKS IN TIME SERIES FORECASTING". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1994. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8489@1.
Pełny tekst źródłaEsta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na previsão de séries temporais, em particular de séries financeiras, consideradas uma classe especial de séries temporais, caracteristicamente ruídos e sem periodicidade aparente. O trabalho envolve quatro partes principais: um estudo sobre redes neurais artificiais e séries temporais; a modelagem das RNAs para previsão de séries temporais; o desenvolvimento de um ambiente de simulação; e o estudo de caso. No estudo sobre Redes Neurais Artificiais e séries temporais fez-se um levantamento preliminar das aplicações de RNAs na previsão de séries. Constatou-se a predominância do uso do algoritmos de retropropagação do erro para o treinamento das redes, bem como dos modelos estatísticos de regressão, de médias móveis e de alisamento exponencial nas comparações com os resultados da rede. Na modelagem das RNAs de retropropagação do erro considerou-se três fatores determinantes no desempenho da rede: convergência, generalização e escalabilidade. Para o controle destes fatores usou-se mecanismos como; escolha da função de ativação dos neurônios - sigmóide ou tangente hiperbólica; escolha da função erro - MSE (Mean Square Error) ou MAD (Mean Absolutd Deviation); e escolha dos parâmetros de controle do gradiente descendente e do temapo de treinamento - taxa de aprendizado e termo de momento. Por fim, definiu-se a arquitetura da rede em função da técnica utilizada para a identificação de regularidades na série (windowing) e da otimização dos fatores indicadores de desempenho da rede. O ambiente de simulação foi desenvolvido em linguagem C e contém 3.600 linhas de códigos divididas em três módulos principais: interface com o usuário, simulação e funções secundárias. O módulo de interface com o usuário é responsável pela configuração e parametrização da rede, como também pela visualização gráfica dos resultados; módulo de simulação executa as fases de treinamento e testes das RNAs; o módulo de funções secundárias cuida do pré/pós-processamento dos dados, da manipulação de arquivos e dos cálculos dos métodos de avaliação empregados. No estudo de caso, as RNAs foram modeladas para fazer previsões da série do preço do ouro no mercado internacional. Foram feitas previsões univariadas single e multi-step e previsões multivariadas utilizando taxas de câmbio de moedas estrangeiras. Os métodos utilizandos para a avaliação do desempenho da rede foram: coeficiente U de Theil, MSE (Mean Square Error), NRMSE (Normalized Root Mean Square Error), POCID (Percentage Of Change In Direction), scattergram e comparação gráfica. Os resultados obtidos, além de avaliados com os métodos acima, foram comparados com o modelo de Box-Jenkins e comprovaram a superioridade das RNAs no tratamento de dados não-lineares e altamente ruidosos.
This dissertation investigates the use of Artificial Neural Nerworks (ANNs) in time series forecastig, especially financial time series, which are typically noisy and with no apparent periodicity. The dissertation covers four major parts: the study of Artificial Neural Networks and time series; the desing of ANNs applied to time series forecasting; the development of a simulation enironment; and a case study. The first part of this dissertation involved the study of Artficial Neural Netwrks and time series theory, resulting in an overview of ANNs utilization in time series forecasting. This overview confirmed the predominance of Backpropagations as the training algorithm, as well as the employment of statistical models, such as regression and moving average, for the Neural Network evaluation. In the design of ANNS, three performance measures were considered: covergence, generalization and scalability. To control these parameters, the following methods were applied: choice of activation function - sigmoid or hiperbolic tangent; choice of cost function - MSE (Mean Square Error) or MAD (Mean Absolute Deviation); choise of parameteres for controlling the gradiente descendent and learning times - the learning rate and momentum term; and network architecture. The simulation environment was developed in C language, with 3,600 lines of code distributed in three main modules: the user interface, the simulaton and the support functions modules. The user interface module is responsaible for the network configuration and for the graphical visualization. The simulation module performs the training and testing of ANNs. The support functions module takes care of the pre and pos processin, the files management and the metrics calculation. The case study concerned with the designing of an ANN to forescast the gold price in the international market. Two kinds of prediction were used: univariate - single and multi-step, and multivariate. The metrics used to evaluate the ANN performance were: U of Theil`s coeficient, MSE (Mean Square Error), NRMSE (Normalized Mean Saquare Error), POCID (Percentage Of Cnage In Direction), scattergram and graphical comparison. The results were also comapred with the Box-Jenkins model, confirming the superiority of ANN in handling non-linear and noisy data.
ZANDONADE, ELIANA. "USING NEURAL NETWORK IN TIME SERIES FORECASTING". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1993. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8641@1.
Pełny tekst źródłaEste trabalho associa previsão de Séries Temporais a uma nova metodologia de processamento de informação: REDE NEURAL. Usaremos o modelo de Retropropagação, que consiste em uma Rede Neural multicamada com as unidades conectadas apenas com a unidades conectadas apenas com as unidades da camada subseqüente e com a informação passando em uma única direção. Aplicaremos o modelo de retropropagação na análise de quatro séries temporais: uma série ruidosa. Uma série com tendência, uma série sazonal e uma série de Consumo de Energia Elétrica da cidade de Uruguaiana, RS. Os resultados obtidos serão comparados com os modelos ARIMA de Box e Jenkins e um modelo com intervenção
This work join the Times-Séries Forecasting to a new information processing metodoligy: NEURAL NETWORK. We will use the Back-Propagation model, that consist in an arquitecture of a feed-forward network with hidden layers. We will apply the Back-Propagation model in an analysis to four times series: a noisy series, a series with trend, a seasonal series and an electrical energy consuption series of Uruguaiana, RS. The results will be compare with the Box and jenkins´ ARIMA models and a model with intervention.
MELLEM, MARCELO TOURASSE NASSIM. "AUTOREGRESSIVE-NEURAL HYBRID MODELS FOR TIME SERIES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1997. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14541@1.
Pełny tekst źródłaEste trabalho apresenta um modelo linear por partes chamado de modelo ARN. Trata-se de uma estrutura híbrida que envolve modelos autoregressivos e redes neurais. Este modelo é comparado com o modelo AR de coeficientes fixos e com a rede neural estática aplicada à previsão. Os resultados mostram que o ARN consegue identificar a estrutura não-linear dos dados simulados e que na maioria dos casos ele possui melhor habilidade preditiva do que os modelos supracitados.
In this thesis we develop a piece-wise linear model named ARN model. Our model has a hybrid structure which combines autoregressive models and neural networks. We compare our model to the fixed-coefficient AR model and to the prediction static neural network. Our results show that ARN is able to find the non-linear structure of simulated data and in most cases it performs better than the methods mentioned above.
Han, Ying. "Analysing time series using artificial neural networks". Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398318.
Pełny tekst źródłaRana, Md Mashud. "Energy time series prediction". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11745.
Pełny tekst źródłaSOTO, CLAVER PARI. "TEMPORAL NEURAL NETWORKS FOR TREATING TIME VARIANT SERIES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7437@1.
Pełny tekst źródłaAs RNA Temporais, em função de sua estrutura, consideram o tempo na sua operação, incorporando memória de curto prazo distribuída na rede em todos os neurônios escondidos e em alguns dos casos nos neurônios de saída. Esta classe de redes é utilizada para representar melhor a natureza temporal dos sistemas dinâmicos. Em contraste, a RNA estática tem uma estrutura apropriada para tarefas de reconhecimento de padrões, classificação e outras de natureza estática ou estacionária tendo sido utilizada com sucesso em diversas aplicações. O objetivo desta tese, portanto foi estudar a teoria e avaliar o desempenho das Redes Neurais Temporais em comparação com as Redes Neurais Estáticas, em aplicações de sistemas dinâmicos. O desenvolvimento desta pesquisa envolveu 3 etapas principais: pesquisa bibliográfica das metodologias desenvolvidas para RNA Temporais; seleção e implementação de modelos para a avaliação destas redes; e estudo de casos. A pesquisa bibliográfica permitiu compila e classificar os principais trabalhos sobre RNA Temporais. Tipicamente, estas redes podem ser classificadas em dois grupos: Redes com Atraso no Tempo e Redes Recorrentes. Para a análise de desempenho, selecionou-se uma redee de cada grupo para implementação. Do primeiro grupo foi selecionada a Rede FIR, onde as sinapses são filtros FIR (Finite-duration Impulse Response) que representam a natureza temporal do problema. A rede FIR foi selecionada por englobar praticamente, todos os outros métodos de sua classe e apresentar um modelo matemático mais formal. Do segundo grupo, considerou-se a rede recorrente de Elman que apresenta realimentação global de cada um dos neurônios escondidos para todos eles. No estudo de casos testou-se o desempenho das redes selecionadas em duas linhas de aplicação: previsão de séries temporais e processamento digital de sinais. No caso de previsão de séries temporais, foram utilizadas séries de consumo de energia elétrica, comparando-se os resultados com os encontrados na literatura a partir de métodos de Holt-Winters, Box & Jenkins e RNA estáticas. No caso da aplicação das RNA em processamento digital de sinais, utilizou-se a filtragem de ruído em sinais de voz onde foram feitas comparações com os resultados apresentados pelo filtro neural convencional, que é uma rede feed-forward multicamada com o algoritmo de retropropagação para o aprendizado. Este trabalho demonstrou na prática que as RNA temporais conseguem capturar as características dos processos temporais de forma mais eficiente que as RNA Estatísticas e outros métodos tradicionais, podendo aprender diretamente o comportamento não estacionário das séries temporais. Os resultados demonstraram que a rede neural FIR e a rede Elman aprendem melhor a complexidade dos sinais de voz.
This dissertation investigates the development of Artificial Neural Network (ANN) in the solution of problems where the patterns presented to the network have a temporary relationship to each other, such as time series forecast and voice processing. Temporary ANN considers the time in its operation, incorporating memory of short period distributed in the network in all the hidden neurons and in the output neurons in some cases. This class of network in better used to represent the temporary nature of the dynamic systems. In contrast, Static ANN has a structure adapted for tasks of pattern recognition, classification and another static or stationary problems, achieving great success in several applications. Considered an universal approximator, Static ANN has also been used in applications of dynamic systems, through some artifices in the input of the network and through statistical data pre- processings. The objective of this work is, therefore to study the theory and evaluate the performance of Temporal ANN, in comparison with Static ANN, in applications of dynamics systems. The development of this research involved 3 main stages: bibliographical research of the methodologies developed for Temporal ANN; selection and implementation of the models for the evaluation of these networks; and case studies. The bibliographical research allowed to compile and to classify the main on Temporal ANN, Typically, these network was selected, where the synapses are filters FIR (Finite-duration Impulse Response) that represent the temporary nature of the problem. The FIR network has been selected since it includes practically all other methods of its class, presenting a more formal mathematical model. On the second group, the Elman recurrent network was considered, that presents global feedback of each neuron in the hidden layer to all other neurons in this layer. In the case studies the network selected have been tested in two application: forecast of time series and digital signal processing. In the case of forecast, result of electric energy consumption time series prediction were compared with the result found in the literature such as Holt-Winters, Box & Jenkins and Static ANN methods. In the case of the application of processing where the comparisons were made with the results presented by the standard neural filter, made of a multilayer feed-forward network with the back propagation learning algorithm. This work showed in practice that Temporal ANN captures the characteristics of the temporary processes in a more efficient way that Static ANN and other methods, being able to learn the non stationary behavior of the temporary series directly. The results showed that the FIR neural network and de Elman network learned better the complexity of the voice signals.
Książki na temat "Neural time series"
Vemuri, V. Artificial neural networks: Forecasting time series. Los Alamitos, Calif: IEEE Computer Society Press, 1994.
Znajdź pełny tekst źródłaAthanasios, Kehagias, red. Predictive modular neural networks: Applications to time series. Boston: Kluwer Academic Publishers, 1998.
Znajdź pełny tekst źródłaNeural, novel & hybrid algorithms for time series prediction. New York: John Wiley & Sons, 1995.
Znajdź pełny tekst źródłaPetridis, Vassilios. Predictive Modular Neural Networks: Applications to Time Series. Boston, MA: Springer US, 1998.
Znajdź pełny tekst źródłaNeural network time series forecasting of financial markets. Chichester: Wiley, 1994.
Znajdź pełny tekst źródłaZanas, N. Artificial neural networks and time series models in economic forecasting. Manchester: UMIST, 1997.
Znajdź pełny tekst źródłaBrunello, Tirozzi, red. Neural networks and sea time series: Reconstruction and extreme event analysis. Boston, MA: Birkhäuser, 2005.
Znajdź pełny tekst źródła1949-, Creedy John, i Martin Vance 1955-, red. Nonlinear economic models: Cross-sectional, time series and neural network applications. Cheltenham, U.K: E. Elgar, 1997.
Znajdź pełny tekst źródłaRoitman, Valéria de Lima. A utilização de redes neurais para previsão de séries temporais. Rio de Janeiro: IPEA, Diretoria de Estudos Macroeconômicos, 2001.
Znajdź pełny tekst źródłaTetko, Igor V., Věra Kůrková, Pavel Karpov i Fabian Theis, red. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30490-4.
Pełny tekst źródłaCzęści książek na temat "Neural time series"
Zhang, Yunong, Dechao Chen i Chengxu Ye. "Application to Time Series Prediction". W Toward Deep Neural Networks, 301–11. Boca Raton, Florida : CRC Press, [2019] | Series: Chapman & Hall/CRC artificial intelligence and robotics series: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429426445-24.
Pełny tekst źródłaGianniotis, Nikolaos. "Linear Dimensionality Reduction for Time Series". W Neural Information Processing, 375–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_40.
Pełny tekst źródłaKyrkou, Lamprini, Christoforos Nalmpantis i Dimitris Vrakas. "Imaging Time-Series for NILM". W Engineering Applications of Neural Networks, 188–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20257-6_16.
Pełny tekst źródłaNalmpantis, Christoforos, i Dimitris Vrakas. "Signal2Vec: Time Series Embedding Representation". W Engineering Applications of Neural Networks, 80–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20257-6_7.
Pełny tekst źródłaKingdon, Jason. "Automating Neural Net Time Series Analysis". W Perspectives in Neural Computing, 107–23. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_6.
Pełny tekst źródłaTaylor, John G. "Univariate and Multivariate Time Series Predictions". W Perspectives in Neural Computing, 11–22. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0151-2_2.
Pełny tekst źródłaHe, Guoliang, Lu Chen, Zhijie Li, Qiaoxian Zheng i Yuanxiang Li. "Computing Skyline Probabilities on Uncertain Time Series". W Neural Information Processing, 61–71. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26555-1_8.
Pełny tekst źródłaCherif, Aymen, Hubert Cardot i Romuald Boné. "Hierarchical Clustering for Local Time Series Forecasting". W Neural Information Processing, 59–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_8.
Pełny tekst źródłaGers, Felix A., Douglas Eck i Jürgen Schmidhuber. "Applying LSTM to Time Series Predictable Through Time-Window Approaches". W Perspectives in Neural Computing, 193–200. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0219-9_20.
Pełny tekst źródłaPanella, Massimo, Fabio Massimo Frattale Mascioli, Antonello Rizzi i Giuseppe Martinelli. "ANFIS Synthesis by Hyperplane Clustering for Time Series Prediction". W Neural Nets, 77–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45216-4_8.
Pełny tekst źródłaStreszczenia konferencji na temat "Neural time series"
Kolarik, Thomas, i Gottfried Rudorfer. "Time series forecasting using neural networks". W the international conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/190271.190290.
Pełny tekst źródłaGodfrey, Luke B., i Michael S. Gashler. "Neural decomposition of time-series data". W 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8123050.
Pełny tekst źródłada Silva Soares, Paulo Ricardo, i Ricardo Bastos Cavalcante Prudencio. "Time Series Based Link Prediction". W 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252471.
Pełny tekst źródłaAbsar, Saima, Yongkai Wu i Lu Zhang. "Neural Time-Invariant Causal Discovery from Time Series Data". W 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10192004.
Pełny tekst źródłaGuijo-Rubio, David, Pedro A. Gutierrez, Anthony Bagnall i Cesar Hervas-Martinez. "Time series ordinal classification via shapelets". W 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207200.
Pełny tekst źródłaShen, Zhipeng, Yuanming Zhang, Jiawei Lu, Jun Xu i Gang Xiao. "SeriesNet:A Generative Time Series Forecasting Model". W 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489522.
Pełny tekst źródłaAchenchabe, Youssef, Alexis Bondu, Antoine Cornuejols i Vincent Lemaire. "Early and Revocable Time Series Classification". W 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892391.
Pełny tekst źródłaRuta, Dymitr, i Bogdan Gabrys. "Neural Network Ensembles for Time Series Prediction". W 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371129.
Pełny tekst źródłaProchazka, A. "Neural networks and seasonal time-series prediction". W Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970698.
Pełny tekst źródłaRakhshani, Hojjat, Hassan Ismail Fawaz, Lhassane Idoumghar, Germain Forestier, Julien Lepagnot, Jonathan Weber, Mathieu Brevilliers i Pierre-Alain Muller. "Neural Architecture Search for Time Series Classification". W 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206721.
Pełny tekst źródłaRaporty organizacyjne na temat "Neural time series"
Rose-Pehrsson, Susan, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk i Mark T. Wright. Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results. Fort Belvoir, VA: Defense Technical Information Center, październik 2000. http://dx.doi.org/10.21236/ada383627.
Pełny tekst źródłaBodruzzaman, M., i M. A. Essawy. Iterative prediction of chaotic time series using a recurrent neural network. Quarterly progress report, January 1, 1995--March 31, 1995. Office of Scientific and Technical Information (OSTI), marzec 1996. http://dx.doi.org/10.2172/283610.
Pełny tekst źródłaChronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell i Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, lipiec 2023. http://dx.doi.org/10.26509/frbc-wp-202315.
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