A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

Koçak, Cem; Dalar, Ali Zafer; Yolcu, Özge Cağcağ; Baş, Eren; Eğrioğlu, Erol

doi:10.1007/s00500-019-04506-1

Cited by 19 publications

(8 citation statements)

References 37 publications

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“…In the process of time series research, the ARIMA model is the optimization of ARMA model [ 27 – 29 ]. Both models are suitable for processing time series data.…”

Section: Construction Of Prediction Models and Scheme Designmentioning

confidence: 99%

Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

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“…In the process of time series research, the ARIMA model is the optimization of ARMA model [ 27 – 29 ]. Both models are suitable for processing time series data.…”

Section: Construction Of Prediction Models and Scheme Designmentioning

confidence: 99%

Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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“…AIC and BIC are based on the concept of entropy and provide criteria for weighing the complexity of the estimation model against the goodness of the fitted data. In recent years, with the popularity of deep learning, ARMA models and artificial neural network associations have been used to analyze time series [20][21][22][23][24]. These studies prove that the combination of the two is effective for time series analysis [25].…”

Section: Introductionmentioning

confidence: 94%

Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model

Yang

Yao

2021

Symmetry

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Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we discussed the fitting effect of the symmetrical high formwork monitoring data using the autoregressive–moving-average (ARMA) model and the back propagation neural networks (BPNN) combined model to process. In the actual project, the symmetry of the high formwork system allows the analysis of local monitoring results to be well extended to the whole. For the establishment of the ARMA model, the accurate judgment of the model order has a significant impact. In this paper, back propagation neural networks (BPNN) are used to simulate the ARMA process. The order of the ARMA model is estimated by determining the optimal neural network structure, which is suitable for linear or nonlinear sequences. We validated this approach from the ARMA model data simulated in Monte Carlo and compared it with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The length of the sequence, the coefficients and the order of the ARMA model are considered as factors that influence the judgment effect. Under different conditions, the BPNN always shows an accuracy rate of more than 90%, while the BIC only has a higher accuracy rate when the model order is low and the judgment efficiency of the AIC is below 50%. Finally, the proposed method successfully modeled the stress sequence and obtained the stress change trend. Compared with AIC and BIC, the efficiency of the processing time series is increased by about 50% when an order is obtained by BPNN.

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“…Por otro lado, el número de nodos de salida depende del número de variables a predecir. Por último, la capa intermedia se usa principalmente para aplicar transformaciones no lineales a las variables de entrada originales (Kocak et al, 2020).…”

Section: Redes Neuronalesunclassified

Entrenamiento de redes neuronales recurrentes usando la dimensión de encaje en el espacio de las fases

2022

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Se propone un método usando Redes Neuronales Recurrentes para la predicción de datos caóticos, aplicando la Teoría del Caos, para estudiar el comportamiento dinámico de los datos en el espacio multidimensional de las fases, establecer la correlación de los mismos y determinar la dimensión de encaje como base para el entrenamiento de las redes neuronales, así como determinar las características dinámicas del sistema calculando los coeficientes de Lyapunov y la entropía de Kolmorov-Siani, que nos indican el grado de desorden que tiene el sistema, para proyectar la precisión de la predicción. Se usan datos de contaminantes PM2.5 tomados en el centro Histórico de la ciudad de Quito, en intervalos de una hora, entre los años 2005 a 2019. Los resultados determinan que las series de datos corresponden a un sistema caótico (más de un coeficiente positivo de Lyapunov), por lo que se justifica la aplicación de la Teoría del Caos en el análisis de los mismos, dando buenos resultados en las predicciones aplicando los métodos de redes neuronales recurrentes de Elman y Jordan, al comparar las series predichas se demuestran que no presentan diferencias significativas entre ellas, ni con los datos medidos, usando el método de varianza con 0,05 de significancia, el error cuadrático porcentual respecto al rango de variación de los datos es aproximadamente del 5 % en ambos casos. Objetivos: Proponer un método que ayude al entrenamiento de las redes neuronales usando la Teoría del caos, mediante la implementación de la dimensión de encaje en el espacio de las fases.

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A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

Cited by 19 publications

References 37 publications

Pop Music Trend and Image Analysis Based on Big Data Technology

Pop Music Trend and Image Analysis Based on Big Data Technology

Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model

Entrenamiento de redes neuronales recurrentes usando la dimensión de encaje en el espacio de las fases

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