[HTML][HTML] Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology
Delay embedding methods are a staple tool in the field of time series analysis and
prediction. However, the selection of embedding parameters can have a big impact on the …
prediction. However, the selection of embedding parameters can have a big impact on the …
Temporal Convolutional Networks with RNN approach for chaotic time series prediction
The prediction of chaotic time series, which constitutes many systems in the field of science
and engineering, has recently become the focus of attention of researchers. Chaotic time …
and engineering, has recently become the focus of attention of researchers. Chaotic time …
[HTML][HTML] Wind power prediction based on EEMD-Tent-SSA-LS-SVM
Z Li, X Luo, M Liu, X Cao, S Du, H Sun - Energy Reports, 2022 - Elsevier
To solve the wind power prediction problem, the Improved Sparrow Search Algorithm-Least
Squares Support Vector Machine (ISSA-LS-SVM) prediction model based on chaotic …
Squares Support Vector Machine (ISSA-LS-SVM) prediction model based on chaotic …
High-efficiency chaotic time series prediction based on time convolution neural network
W Cheng, Y Wang, Z Peng, X Ren, Y Shuai… - Chaos, Solitons & …, 2021 - Elsevier
The prediction of chaotic time series is important for both science and technology. In recent
years, this type of prediction has improved significantly with the development of deep …
years, this type of prediction has improved significantly with the development of deep …
[HTML][HTML] Forecasting of noisy chaotic systems with deep neural networks
Recurrent neural networks have recently proved the state-of-the-art approach in forecasting
complex oscillatory time series on a multi-step horizon. Researchers in the field investigated …
complex oscillatory time series on a multi-step horizon. Researchers in the field investigated …
[HTML][HTML] Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate, regime transitions, and the effect of …
We develop and test machine learning techniques for successfully using past state time
series data and knowledge of a time-dependent system parameter to predict the evolution of …
series data and knowledge of a time-dependent system parameter to predict the evolution of …
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 …
engineering. In recent years, machine learning algorithms have proven to be a very …
Dynamical analysis and applications of a novel 2-D hybrid dual-memristor hyperchaotic map with complexity enhancement
The dual-memristor hyperchaotic map has not yet received much attention and application,
and its complexity and flexibility deserve further improvement. To this end, a novel two …
and its complexity and flexibility deserve further improvement. To this end, a novel two …
[HTML][HTML] AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition
The integration of multi-source sensors based AIoT (Artificial Intelligence of Things)
technologies into air quality measurement and forecasting is becoming increasingly critical …
technologies into air quality measurement and forecasting is becoming increasingly critical …
Deep networks for system identification: a survey
Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …
collections and powerful software resources has led to an impressive amount of results in …