Chaotic time series forecasting approaches using machine learning techniques: A review

B Ramadevi, K Bingi - Symmetry, 2022 - mdpi.com
Traditional statistical, physical, and correlation models for chaotic time series prediction
have problems, such as low forecasting accuracy, computational time, and difficulty …

Statistical methods with applications in data mining: A review of the most recent works

JF Pinto da Costa, M Cabral - Mathematics, 2022 - mdpi.com
The importance of statistical methods in finding patterns and trends in otherwise
unstructured and complex large sets of data has grown over the past decade, as the amount …

Chaos theory meets deep learning: A new approach to time series forecasting

B Jia, H Wu, K Guo - Expert Systems with Applications, 2024 - Elsevier
We explore the influence and advantages of integrating chaotic systems with deep learning
for time series forecasting in this paper. It proposes a novel deep learning method based on …

Dynamical time series embeddings in recurrent neural networks

G Uribarri, GB Mindlin - Chaos, Solitons & Fractals, 2022 - Elsevier
Time series forecasting has historically been a key research problem in science and
engineering. In recent years, machine learning algorithms have proven to be a very …

A dynamically stabilized recurrent neural network

S Saab Jr, Y Fu, A Ray, M Hauser - Neural Processing Letters, 2022 - Springer
This work proposes a novel recurrent neural network architecture, called the Dynamically
Stabilized Recurrent Neural Network (DSRNN). The developed DSRNN includes learnable …

A novel time series prediction method based on pooling compressed sensing echo state network and its application in stock market

Z Wang, H Zhao, M Zheng, S Niu, X Gao, L Li - Neural Networks, 2023 - Elsevier
In the prediction of time series, the echo state network (ESN) exhibits exclusive strengths
and a unique training structure. Based on ESN model, a pooling activation algorithm …

Growing deep echo state network with supervised learning for time series prediction

Y Li, F Li - Applied Soft Computing, 2022 - Elsevier
Multilayer echo state networks (ESNs) are powerful on learning hierarchical temporal
representation. However, how to determine the depth of multilayer ESNs is still an open …

Deep learning-based state prediction of the Lorenz system with control parameters

X Wang, J Feng, Y Xu, J Kurths - Chaos: An Interdisciplinary Journal of …, 2024 - pubs.aip.org
Nonlinear dynamical systems with control parameters may not be well modeled by shallow
neural networks. In this paper, the stable fixed-point solutions, periodic and chaotic solutions …

Forecasting for chaotic time series based on GRP-lstmGAN model: Application to temperature series of rotary kiln

W Hu, Z Mao - Entropy, 2022 - mdpi.com
Rotary kiln temperature forecasting plays a significant part of the automatic control of the
sintering process. However, accurate forecasts are difficult owing to the complex nonlinear …

Recognizing chaos by deep learning and transfer learning on recurrence plots

Y Zhou, S Gao, M Sun, Y Zhou, Z Chen… - International Journal of …, 2023 - World Scientific
Chaos recognition is necessary to determine the prediction possibility for specific time
series. In this paper, we attempt to seek a novel chaos recognition method based on the …