scholarly journals Using Lyapunov’s method for analysing of chaotic behaviour on financial time series data: a case study on Tehran stock exchange

2020 ◽  
Vol 2 (3) ◽  
pp. 297-308
Author(s):  
Mohammad Reza Abbaszadeh ◽  
◽  
Mehdi Jabbari Nooghabi ◽  
Mohammad Mahdi Rounaghi ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Chaotic Behaviour ◽  
Series Data ◽  
Financial Time ◽  
Lyapunov's Method ◽  
Lyapunov’S Method

scholarly journals A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 103 ◽  
Author(s):  
Mengxing Huang ◽  
Qili Bao ◽  
Yu Zhang ◽  
Wenlong Feng
Keyword(s):  
Time Series ◽  
Hybrid Algorithm ◽  
Prediction Models ◽  
Time Series Data ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Variable Parameter ◽  
Series Data ◽  
Support Vector ◽  
Financial Time

Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.

scholarly journals An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES

2005 ◽  
Vol 50 (01) ◽  
pp. 1-8 ◽  
Author(s):  
PETER M. ROBINSON
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Nonlinear Models ◽  
Series Data ◽  
Financial Variables ◽  
Financial Time ◽  
Linear And Nonlinear

Much time series data are recorded on economic and financial variables. Statistical modeling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of "memory", or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.

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