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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

The integration of multi-source sensors based AIoT (Artificial Intelligence of Things)
technologies into air quality measurement and forecasting is becoming increasingly critical …

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 …