Trajectory-based visual analysis of large financial time series data

In the era of big data, the analysis of multi-dimensional time series data is one of the important topics in many fields such as finance, science, logistics, and engineering. Using stacked graphs for visual analysis helps to visually reveal the changing characteristics of each dimension over time. In order to present visually appealing and easy-to-read stacked graphs, this paper constructs the minimum cumulative variance rule to determine the stacking order of each dimension, as well as adopts the width priority principle and the color complementary principle to determine the label placement positioning and text coloring. In addition, a color matching method is recommended by user study. The proposed optimal visual layout algorithm is applied to the visual analysis of actual multidimensional financial time series data, and as a result, vividly reveals the characteristics of the flow of securities trading funds between sectors.

Download Full-text

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

Applied Sciences ◽

10.3390/app11093876 ◽

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.

Download Full-text

Wavelet transform approach for fitting financial time series data

10.1063/1.4932493 ◽

2015 ◽

Author(s):

Amel Abdoullah Ahmed ◽

Mohd Tahir Ismail

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Time Series Data ◽

Financial Time Series ◽

Series Data ◽

Financial Time

Download Full-text

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

Discrete Dynamics in Nature and Society ◽

10.1155/2012/593018 ◽

2012 ◽

Vol 2012 ◽

pp. 1-21 ◽

Cited By ~ 7

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.

Download Full-text

MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES

The Singapore Economic Review ◽

10.1142/s0217590805001809 ◽

2005 ◽

Vol 50 (01) ◽

pp. 1-8 ◽

Cited By ~ 1

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.

Download Full-text