Relevant bibliographies by topics / Neural time series

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

Academic literature on the topic 'Neural time series'

Author: Grafiati
Published: 18 May 2024

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Journal articles on the topic "Neural time series"

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Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (May 7, 2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.

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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.
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Golovenko, A. O., and A. A. Kopyrkin. "Neural Network Forecasting of Time Series." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 19, no. 4 (2019): 124–31. http://dx.doi.org/10.14529/ctcr190412.

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Kolarik, Thomas, and Gottfried Rudorfer. "Time series forecasting using neural networks." ACM SIGAPL APL Quote Quad 25, no. 1 (October 1994): 86–94. http://dx.doi.org/10.1145/190468.190290.

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Xinhui, Wen, and Chen Kaizhou. "Time series neural network forecasting methods." Journal of Electronics (China) 12, no. 1 (January 1995): 1–8. http://dx.doi.org/10.1007/bf02684561.

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Liu, Zhi Cheng. "Real Time Prediction Method of Sensor Output Time Series." Advanced Materials Research 912-914 (April 2014): 1322–26. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1322.

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In order to improve the real time prediction precision of sensor output time series, the predictable inner mechanism of time series is analyzed, and a method using wavelet filtering and neural network is proposed. Sensor output time series are first handled with wavelet filtering, and then predicted by neural network method. The proposed method can eliminate effect of measurement noise on prediction precision. Simulation experiment shows a higher prediction precision by the method. A new idea is given to increase prediction precision of sensor output time series by neural network-based methods.
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Panigrahi, Sibarama, Yasobanta Karali, and H. S. Behera. "Time Series Forecasting using Evolutionary Neural Network." International Journal of Computer Applications 75, no. 10 (August 23, 2013): 13–17. http://dx.doi.org/10.5120/13146-0553.

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Kim, JongHwa, Jong Hoo Choi, and Changwan Kang. "Time Series Prediction Using Recurrent Neural Network." Korean Data Analysis Society 21, no. 4 (August 31, 2019): 1771–79. http://dx.doi.org/10.37727/jkdas.2019.21.4.1771.

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Zhang, Dongqing, and Yubing Han. "Time Series Prediction with RBF Neural Networks." Information Technology Journal 12, no. 14 (July 1, 2013): 2815–19. http://dx.doi.org/10.3923/itj.2013.2815.2819.

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Sako, Kady, Berthine Nyunga Mpinda, and Paulo Canas Rodrigues. "Neural Networks for Financial Time Series Forecasting." Entropy 24, no. 5 (May 7, 2022): 657. http://dx.doi.org/10.3390/e24050657.

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Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.
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Pérez-Chavarría, M. A. "Time series prediction using artificial neural networks." Ciencias Marinas 28, no. 1 (February 1, 2002): 67–77. http://dx.doi.org/10.7773/cm.v28i1.205.

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Dissertations / Theses on the topic "Neural time series"

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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.

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Andreux, Mathieu. "Foveal autoregressive neural time-series modeling." Electronic Thesis or Diss., Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE073.

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Cette thèse s'intéresse à la modélisation non-supervisée de séries temporelles univariées. Nous abordons tout d'abord le problème de prédiction linéaire des valeurs futures séries temporelles gaussiennes sous hypothèse de longues dépendances, qui nécessitent de tenir compte d'un large passé. Nous introduisons une famille d'ondelettes fovéales et causales qui projettent les valeurs passées sur un sous-espace adapté au problème, réduisant ainsi la variance des estimateurs associés. Dans un deuxième temps, nous cherchons sous quelles conditions les prédicteurs non-linéaires sont plus performants que les méthodes linéaires. Les séries temporelles admettant une représentation parcimonieuse en temps-fréquence, comme celles issues de l'audio, réunissent ces conditions, et nous proposons un algorithme de prédiction utilisant une telle représentation. Le dernier problème que nous étudions est la synthèse de signaux audios. Nous proposons une nouvelle méthode de génération reposant sur un réseau de neurones convolutionnel profond, avec une architecture encodeur-décodeur, qui permet de synthétiser de nouveaux signaux réalistes. Contrairement à l'état de l'art, nous exploitons explicitement les propriétés temps-fréquence des sons pour définir un encodeur avec la transformée en scattering, tandis que le décodeur est entraîné pour résoudre un problème inverse dans une métrique adaptée
This 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
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Bonato, Tommaso. "Time Series Predictions With Recurrent Neural Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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L'obiettivo principale di questa tesi è studiare come gli algoritmi di apprendimento automatico (machine learning in inglese) e in particolare le reti neurali LSTM (Long Short Term Memory) possano essere utilizzati per prevedere i valori futuri di una serie storica regolare come, per esempio, le funzioni seno e coseno. Una serie storica è definita come una sequenza di osservazioni s_t ordinate nel tempo. Inoltre cercheremo di applicare gli stessi principi per prevedere i valori di una serie storica prodotta utilizzando i dati di vendita di un prodotto cosmetico durante un periodo di tre anni. Prima di arrivare alla parte pratica di questa tesi è necessario introdurre alcuni concetti fondamentali che saranno necessari per sviluppare l'architettura e il codice del nostro modello. Sia nell'introduzione teorica che nella parte pratica l'attenzione sarà focalizzata sull'uso di RNN (Recurrent Neural Network o Rete Neurale Ricorrente) poiché sono le reti neurali più adatte a questo tipo di problema. Un particolare tipo di RNN, chiamato Long Short Term Memory (LSTM), sarà soggetto dello studio principale di questa tesi e verrà presentata e utilizzata anche una delle sue varianti chiamata Gated Recurrent Unit (GRU). Questa tesi, in conclusione, conferma che LSTM e GRU sono il miglior tipo di rete neurale per le previsioni di serie temporali. Nell'ultima parte analizzeremo le differenze tra l'utilizzo di una CPU e una GPU durante la fase di training della rete neurale.
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Brax, 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.

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Recurrent 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.

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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.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Esta 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.
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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.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Este 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.
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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.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Este 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.
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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.

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Rana, Md Mashud. "Energy time series prediction." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11745.

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Reliable operations and economical utilization of power systems require electricity load forecasting at a wide range of forecasting horizons. The objective of this thesis is two-fold: developing accurate prediction models for electricity load forecasting, and quantifying the load forecasting uncertainty. At first, we consider the task of feature selection for electricity load forecasting. We propose a two-step approach - identifying a set of candidate features based on the data characteristics and then selecting a subset of them using four different methods. We evaluate the performance of these methods using state-of-the-art prediction algorithms. The results show that all feature selection methods are able to identify small subsets of highly relevant features for electricity load forecasting. We then present a generic approach for very short term electricity load forecasting. It combines multilevel wavelet packet transform, a non-linear feature selection method based on mutual information, and machine learning prediction algorithms. The evaluation shows that the proposed approach is robust and outperforms several non-wavelet based approaches. We also propose a novel approach for forecasting the daily load profile. The proposed approach uses mutual information for feature selection and an ensemble of neural networks for building a prediction model. The evaluation using two years of electricity load data for Australia, Portugal and Spain shows that it provides accurate predictions. Finally, we present LUBEX, a neural networks based approach for forecasting prediction intervals to quantify the uncertainty associated with electricity load prediction. LUBEX extends an existing method (LUBE) by including an advanced feature selection method and using an ensemble of neural networks. A comprehensive evaluation using 24 different case studies shows that LUBEX is able to generate high quality prediction intervals.
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SOTO, 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.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
As 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.
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Books on the topic "Neural time series"

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Vemuri, V. Artificial neural networks: Forecasting time series. Los Alamitos, Calif: IEEE Computer Society Press, 1994.

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Athanasios, Kehagias, ed. Predictive modular neural networks: Applications to time series. Boston: Kluwer Academic Publishers, 1998.

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Neural, novel & hybrid algorithms for time series prediction. New York: John Wiley & Sons, 1995.

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Petridis, Vassilios. Predictive Modular Neural Networks: Applications to Time Series. Boston, MA: Springer US, 1998.

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Neural network time series forecasting of financial markets. Chichester: Wiley, 1994.

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Zanas, N. Artificial neural networks and time series models in economic forecasting. Manchester: UMIST, 1997.

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Brunello, Tirozzi, ed. Neural networks and sea time series: Reconstruction and extreme event analysis. Boston, MA: Birkhäuser, 2005.

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1949-, Creedy John, and Martin Vance 1955-, eds. Nonlinear economic models: Cross-sectional, time series and neural network applications. Cheltenham, U.K: E. Elgar, 1997.

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Roitman, Valéria de Lima. A utilização de redes neurais para previsão de séries temporais. Rio de Janeiro: IPEA, Diretoria de Estudos Macroeconômicos, 2001.

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Tetko, Igor V., Věra Kůrková, Pavel Karpov, and Fabian Theis, eds. 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.

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Book chapters on the topic "Neural time series"

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Zhang, Yunong, Dechao Chen, and Chengxu Ye. "Application to Time Series Prediction." In 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.

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Gianniotis, Nikolaos. "Linear Dimensionality Reduction for Time Series." In Neural Information Processing, 375–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_40.

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Kyrkou, Lamprini, Christoforos Nalmpantis, and Dimitris Vrakas. "Imaging Time-Series for NILM." In Engineering Applications of Neural Networks, 188–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20257-6_16.

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Nalmpantis, Christoforos, and Dimitris Vrakas. "Signal2Vec: Time Series Embedding Representation." In Engineering Applications of Neural Networks, 80–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20257-6_7.

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Kingdon, Jason. "Automating Neural Net Time Series Analysis." In Perspectives in Neural Computing, 107–23. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_6.

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Taylor, John G. "Univariate and Multivariate Time Series Predictions." In Perspectives in Neural Computing, 11–22. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0151-2_2.

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He, Guoliang, Lu Chen, Zhijie Li, Qiaoxian Zheng, and Yuanxiang Li. "Computing Skyline Probabilities on Uncertain Time Series." In Neural Information Processing, 61–71. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26555-1_8.

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Cherif, Aymen, Hubert Cardot, and Romuald Boné. "Hierarchical Clustering for Local Time Series Forecasting." In Neural Information Processing, 59–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_8.

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Gers, Felix A., Douglas Eck, and Jürgen Schmidhuber. "Applying LSTM to Time Series Predictable Through Time-Window Approaches." In Perspectives in Neural Computing, 193–200. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0219-9_20.

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Panella, Massimo, Fabio Massimo Frattale Mascioli, Antonello Rizzi, and Giuseppe Martinelli. "ANFIS Synthesis by Hyperplane Clustering for Time Series Prediction." In Neural Nets, 77–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45216-4_8.

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Conference papers on the topic "Neural time series"

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Kolarik, Thomas, and Gottfried Rudorfer. "Time series forecasting using neural networks." In the international conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/190271.190290.

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Godfrey, Luke B., and Michael S. Gashler. "Neural decomposition of time-series data." In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8123050.

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da Silva Soares, Paulo Ricardo, and Ricardo Bastos Cavalcante Prudencio. "Time Series Based Link Prediction." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252471.

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Absar, Saima, Yongkai Wu, and Lu Zhang. "Neural Time-Invariant Causal Discovery from Time Series Data." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10192004.

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Guijo-Rubio, David, Pedro A. Gutierrez, Anthony Bagnall, and Cesar Hervas-Martinez. "Time series ordinal classification via shapelets." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207200.

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Shen, Zhipeng, Yuanming Zhang, Jiawei Lu, Jun Xu, and Gang Xiao. "SeriesNet:A Generative Time Series Forecasting Model." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489522.

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Achenchabe, Youssef, Alexis Bondu, Antoine Cornuejols, and Vincent Lemaire. "Early and Revocable Time Series Classification." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892391.

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Ruta, Dymitr, and Bogdan Gabrys. "Neural Network Ensembles for Time Series Prediction." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371129.

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Prochazka, A. "Neural networks and seasonal time-series prediction." In Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970698.

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Rakhshani, Hojjat, Hassan Ismail Fawaz, Lhassane Idoumghar, Germain Forestier, Julien Lepagnot, Jonathan Weber, Mathieu Brevilliers, and Pierre-Alain Muller. "Neural Architecture Search for Time Series Classification." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206721.

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Reports on the topic "Neural time series"

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Rose-Pehrsson, Susan, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, and 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, October 2000. http://dx.doi.org/10.21236/ada383627.

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Bodruzzaman, M., and 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), March 1996. http://dx.doi.org/10.2172/283610.

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Chronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell, and Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, July 2023. http://dx.doi.org/10.26509/frbc-wp-202315.

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In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time variation in the effectiveness of specific containment policies.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Osipov, Gennadij Sergeevich, Natella Semenovna Vashakidze, and Galina Viktorovna Filippova. Basics of forecasting financial time series based on NeuroXL Predictor. Постулат, 2017. http://dx.doi.org/10.18411/postulat-2017-7.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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Mohanty, Subhasish, and Joseph Listwan. Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1822853.

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Arhin, Stephen, Babin Manandhar, Kevin Obike, and Melissa Anderson. Impact of Dedicated Bus Lanes on Intersection Operations and Travel Time Model Development. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2040.

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Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the District of Columbia leading to the installation of red-painted DBLs on corridors of H Street, NW, and I Street, NW. This study evaluates the impacts on the performance of transit buses along with the general traffic performance at intersections on corridors with DBLs installed in Washington, DC by using a “before” and “after” approach. The team utilized non-intrusive video data to perform vehicular turning movement counts to assess the traffic flow and delays (measures of effectiveness) with a traffic simulation software. Furthermore, the team analyzed the Automatic Vehicle Locator (AVL) data provided by WMATA for buses operating on the study segments to evaluate bus travel time. The statistical analysis showed that the vehicles traveling on H Street and I Street (NW) experienced significantly lower delays during both AM (7:00–9:30 AM) and PM (4:00–6:30 PM) peak hours after the installation of bus lanes. The approximation error metrics (normalized squared errors) for the testing dataset was 0.97, indicating that the model was predicting bus travel times based on unknown data with great accuracy. WMATA can apply this research to other segments with busy bus schedules and multiple routes to evaluate the need for DBLs. Neural network models can also be used to approximate bus travel times on segments by simulating scenarios with DBLs to obtain accurate bus travel times. Such implementation could not only improve WMATA’s bus service and reliability but also alleviate general traffic delays.
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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Joshi, Anuradha, Jalia Kangave, and Vanessa van den Boogaard. Engendering Taxation: a Research and Policy Agenda. Institute of Development Studies, March 2024. http://dx.doi.org/10.19088/ictd.2024.017.

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Increased attention has been paid to the gender dimensions of taxation in recent years, though there has been limited research on the subject – particularly in lower-income contexts. Understanding how tax policies might affect women in lower-income countries is important at the current time, when governments are looking for new ways to increase domestic revenue – particularly through expanding the tax base. Given that women have historically represented only a small part of the formal workforce in these contexts, a shift towards indirect taxes and taxing the informal economy are likely to have a disproportionate effect on poorer households, and women in particular. Understanding whether, and in what specific ways, tax policy in lower-income countries affects the ability of women to participate in the workforce and carry out their caring responsibilities within households is critical for ensuring development with gender justice. This paper reviews the existing literature and related debates on gender and tax in lower income countries. It identifies knowledge gaps, and maps broader issues that are relevant for understanding the gendered impact of taxation. The paper makes four broad observations. First, existing research focuses on formal direct taxes that are less relevant for women in lower-income contexts, given their high participation rates in the informal economy. Instead, presumptive taxes, user fees and informal taxes place a disproportionate burden on low income women. Second, there needs to be greater attention paid to the ways in which women in senior and junior positions in tax administration can affect how taxpayers interact with tax authorities. Third, any assessment of tax policy’s impact on gender needs to consider revenue and expenditure together to ensure that the positive effects of tax policies are not undermined by budgets, or vice versa. Finally, we show that there has been insufficient gender-disaggregated data collection and analysis, which is required to draw generalizable conclusions about the gendered impact of tax policy. We argue that tax specialists need to think about research questions that address these gaps, and simultaneously address methodological challenges by gender disaggregation in data collection, as well as impact evaluation of tax policy implementation and innovation. Our overall conclusions are that tax policies can be made gender-neutral by paying careful attention to where they affect women differentially. There are opportunities for governments to explore policies that positively discriminate as a way to address structural gendered inequities. At the same time we recognise that, barring a few exceptions, tax policy and administration is often an unwieldy instrument to address gender equity directly. Instead other policies relating to labour markets, social protection and public services are better placed to be gender-transformative.
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