Traditional statistical, physical, and correlation models for chaotic time series prediction
have problems, such as low forecasting accuracy, computational time, and difficulty …

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 …

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 …

This paper discusses the chaos prediction of nonlinear systems using various neural
networks based on the modified substructure data-driven modeling architecture. In the …

Residual analysis using hybrid Elman–NARX neural network along with embedding
theorem is used to analyze and predict chaotic time series. Using embedding theorem, the …

Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …

Accurate time series forecasting is crucial, particularly in real-world application areas such
as demand forecasting. The Prophet model successfully predicts time series containing well …

Financial time series are chaotic that, in turn, leads their predictability to be complex and
challenging. This paper presents a novel financial time series prediction hybrid that involves …

Chaos is widespread in non-linear systems such as finance, energy, and weather. In the
chaos system, a variable changing with time generates a chaotic time series, which contains …

In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and
adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series …