Long-Term Time Series Prediction Using k-NN Based LS-SVM Framework with Multi-Value Integration

Long-term time-series prediction using radial basis function neural networks

10.1063/1.4912958 ◽

2015 ◽

Cited By ~ 1

Author(s):

Alex Alexandridis ◽

Ioannis Th. Famelis ◽

Charalambos Tsitouras

Keyword(s):

Neural Networks ◽

Time Series ◽

Radial Basis Function ◽

Basis Function ◽

Time Series Prediction ◽

Radial Basis

DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

Expert Systems with Applications ◽

10.1016/j.eswa.2019.113082 ◽

2020 ◽

Vol 143 ◽

pp. 113082 ◽

Cited By ~ 12

Author(s):

Yeqi Liu ◽

Chuanyang Gong ◽

Ling Yang ◽

Yingyi Chen

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Prediction ◽

Multivariate Time Series ◽

Two Phase ◽

Dual Stage

Long-term time series prediction using wrappers for variable selection and clustering for data partition

2007 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2007.4371450 ◽

2007 ◽

Cited By ~ 3

Author(s):

Wilfredo J. Puma-Villanueva ◽

Euripedes P. dos Santos ◽

Fernando J. Von Zuben

Keyword(s):

Time Series ◽

Variable Selection ◽

Time Series Prediction ◽

Data Partition

A Study on Long-Term Fluctuation Estimator in Time-Series Prediction Network

Transactions of the Society of Instrument and Control Engineers ◽

10.9746/sicetr1965.38.333 ◽

2002 ◽

Vol 38 (3) ◽

pp. 333-335 ◽

Cited By ~ 1

Author(s):

Takayuki FURUYA ◽

Hajime KANADA ◽

Takehiko OGAWA

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Term Fluctuation

A Recurrent-Cascade-Neural network- nonlinear autoregressive networks with exogenous inputs (NARX) approach for long-term time-series prediction of wave height based on wave characteristics measurements

Ocean Engineering ◽

10.1016/j.oceaneng.2021.109958 ◽

2021 ◽

Vol 240 ◽

pp. 109958

Author(s):

Yehia Miky ◽

Mosbeh R. Kaloop ◽

Mohamed T. Elnabwy ◽

Ahmad Baik ◽

Ahmed Alshouny

Keyword(s):

Neural Network ◽

Time Series ◽

Wave Height ◽

Time Series Prediction ◽

Wave Characteristics ◽

Cascade Neural Network ◽

Nonlinear Autoregressive

Double quantization of the regressor space for long-term time series prediction: method and proof of stability

Neural Networks ◽

10.1016/j.neunet.2004.08.008 ◽

2004 ◽

Vol 17 (8-9) ◽

pp. 1169-1181 ◽

Cited By ~ 12

Author(s):

Geoffroy Simon ◽

Amaury Lendasse ◽

Marie Cottrell ◽

Jean-Claude Fort ◽

Michel Verleysen

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Prediction Method ◽

Double Quantization

Long-term time series prediction with the NARX network: An empirical evaluation

Neurocomputing ◽

10.1016/j.neucom.2008.01.030 ◽

2008 ◽

Vol 71 (16-18) ◽

pp. 3335-3343 ◽

Cited By ~ 191

Author(s):

José Maria P. Menezes ◽

Guilherme A. Barreto

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Empirical Evaluation

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review

Entropy ◽

10.3390/e23121672 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1672

Author(s):

Sebastian Raubitzek ◽

Thomas Neubauer

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Time Series Prediction ◽

Long Term Memory ◽

Learning Approaches ◽

Entropy Measures ◽

Combine Machine ◽

Signal Complexity

Measures of signal complexity, such as the Hurst exponent, the fractal dimension, and the Spectrum of Lyapunov exponents, are used in time series analysis to give estimates on persistency, anti-persistency, fluctuations and predictability of the data under study. They have proven beneficial when doing time series prediction using machine and deep learning and tell what features may be relevant for predicting time-series and establishing complexity features. Further, the performance of machine learning approaches can be improved, taking into account the complexity of the data under study, e.g., adapting the employed algorithm to the inherent long-term memory of the data. In this article, we provide a review of complexity and entropy measures in combination with machine learning approaches. We give a comprehensive review of relevant publications, suggesting the use of fractal or complexity-measure concepts to improve existing machine or deep learning approaches. Additionally, we evaluate applications of these concepts and examine if they can be helpful in predicting and analyzing time series using machine and deep learning. Finally, we give a list of a total of six ways to combine machine learning and measures of signal complexity as found in the literature.

Multi-model integration for long-term time series prediction

2012 IEEE 13th International Conference on Information Reuse & Integration (IRI) ◽

10.1109/iri.2012.6302999 ◽

2012 ◽

Cited By ~ 1

Author(s):

Zifang Huang ◽

Mei-Ling Shyu ◽

James M. Tien

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Model Integration

A Toy Model Study for Long-Term Terror Event Time Series Prediction with CNN

European Journal for Security Research ◽

10.1007/s41125-019-00061-w ◽

2019 ◽

Vol 5 (2) ◽

pp. 289-309

Author(s):

Aishvarya Kumar Jain ◽

Christian Grumber ◽

Patrick Gelhausen ◽

Ivo Häring ◽

Alexander Stolz

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Event Time ◽

Model Study