scholarly journals Composite Multiscale Partial Cross-Sample Entropy Analysis for Quantifying Intrinsic Similarity of Two Time Series Affected by Common External Factors

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1003 ◽  
Author(s):  
Baogen Li ◽  
Guosheng Han ◽  
Shan Jiang ◽  
Zuguo Yu
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
Sample Entropy ◽  
External Factors ◽  
Experimental Results ◽  
Entropy Analysis ◽  
Artificial Data ◽  
The Third

In this paper, we propose a new cross-sample entropy, namely the composite multiscale partial cross-sample entropy (CMPCSE), for quantifying the intrinsic similarity of two time series affected by common external factors. First, in order to test the validity of CMPCSE, we apply it to three sets of artificial data. Experimental results show that CMPCSE can accurately measure the intrinsic cross-sample entropy of two simultaneously recorded time series by removing the effects from the third time series. Then CMPCSE is employed to investigate the partial cross-sample entropy of Shanghai securities composite index (SSEC) and Shenzhen Stock Exchange Component Index (SZSE) by eliminating the effect of Hang Seng Index (HSI). Compared with the composite multiscale cross-sample entropy, the results obtained by CMPCSE show that SSEC and SZSE have stronger similarity. We believe that CMPCSE is an effective tool to study intrinsic similarity of two time series.

scholarly journals Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 245
Author(s):  
Ildoo Kim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Soap Film ◽  
Sample Entropy ◽  
Series Data ◽  
Entropy Analysis ◽  
Measured Time ◽  
Energetic Analysis ◽  
Turbulence Data ◽  
Decaying Turbulence

Multiscale sample entropy analysis has been developed to quantify the complexity and the predictability of a time series, originally developed for physiological time series. In this study, the analysis was applied to the turbulence data. We measured time series data for the velocity fluctuation, in either the longitudinal or transverse direction, of turbulent soap film flows at various locations. The research was to assess the feasibility of using the entropy analysis to qualitatively characterize turbulence, without using any conventional energetic analysis of turbulence. The study showed that the application of the entropy analysis to the turbulence data is promising. From the analysis, we successfully captured two important features of the turbulent soap films. It is indicated that the turbulence is anisotropic from the directional disparity. In addition, we observed that the most unpredictable time scale increases with the downstream distance, which is an indication of the decaying turbulence.

scholarly journals Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 798
Author(s):  
José Javier Reyes-Lagos ◽  
Adriana Cristina Pliego-Carrillo ◽  
Claudia Ivette Ledesma-Ramírez ◽  
Miguel Ángel Peña-Castillo ◽  
María Teresa García-González ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Coarse Graining ◽  
Entropy Analysis ◽  
Third Trimester ◽  
K Value ◽  
Order Difference ◽  
Robust Approach ◽  
The Third ◽  
Nonlinear Features

Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed to analyze the nonlinear features of raw electrohysterogram (EHG) time series collected from women at the third trimester of pregnancy (TT) and later during term active parturition (P) by PhEn. We collected 10-min longitudinal transabdominal recordings of 24 low-risk pregnant women at TT (from 35 to 38 weeks of pregnancy) and P (>39 weeks of pregnancy). We computed the second-order difference plots (SODPs) for the TT and P stages, and we evaluated the PhEn by modifying the k value, a coarse-graining parameter. Our results pointed out that PhEn in TT is characterized by a higher likelihood of manifesting nonlinear dynamics compared to the P condition. However, both conditions maintain percentages of nonlinear series higher than 66%. We conclude that the nonlinear features appear to be retained for both stages of pregnancy despite the uterine and cervical reorganization process that occurs in the transition from the third trimester to parturition.

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

scholarly journals Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

scholarly journals Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Composite Index ◽  
Support Vector ◽  
Financial Time ◽  
Svm Model

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

scholarly journals Penerapan WEMA dalam Peramalan Data IHSG

2013 ◽  
Vol 5 (2) ◽  
pp. 63-66
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Average Method ◽  
Stock Exchange ◽  
Moving Average ◽  
Composite Index ◽  
Market Change ◽  
Series Analysis ◽  
Future Data ◽  
Technical Indicator

One of the most popular technical indicator used in time series analysis for predicting future data is the Moving Average method. During its’ development, many variation and implementation have been made by researchers, one of them is the Weighted Exponential Moving Average (WEMA) which is introduced by Hansun.In this paper, we will try to implement the WEMA method on one of stock market change indicator in Indonesia, i.e. the Jakarta Stock Exchange (JKSE) composite index data. The research is continued by calculating the accuracy and robustness of WEMA method, using MSE and MAPE criteria. The result shows that the WEMA method can be used to predict JKSE data and it’s quite accurate. Kata kunci—time series analysis, JKSE, moving average, WEMA

PENGARUH VOLATILITAS NILAI TUKAR, IDR-USD TERHADAP RETURN SAHAM DI BURSA EFEK INDONESIA: PENERAPAN MODEL GARCH

2019 ◽  
Vol 14 (2) ◽  
pp. 95
Author(s):  
Rahmadiva Dianitha Danial ◽  
Brady Rikumahu
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Granger Causality ◽  
Stock Price ◽  
Foreign Exchange Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Granger Causality Test ◽  
Causality Test ◽  
Stock Market Returns

Penelitian ini bertujuan untuk menguji pengaruh  volatilitas return nilai Kurs IDR-USD terhadap volatilitas return pasar saham di Bursa Efek Indonesia. Dari pengambilan data sekunder dari 3 Januari 2012 hingga 29 September 2017 diperoleh data time series sebanyak 1404 hari. Data  dianalisis dengan model  GARCH dan Uji Granger Causality. Berdasarkan hasil permodelan GARCH(1,1), volatilitas kurs mempengaruhi volatilitas IHSG. Uji Granger Causality menunjukkan bahwa volatilitas kurs  dan IHSG memiliki hubungan yang kausal dua arah. Penelitian ini menunjukkan bahwa informasi kurs dapat memprediksikan kondisi harga indeks saham di pasar modal di periode hari berikutnya, begitupun sebaliknya. Prediksi tepat yang dilakukan oleh investor akan mengurangi risiko dan meningkatkan imbal hasil dalam berinvestasi jika pasar uang maupun pasar modal yang sedang bergejolak.  Kata Kunci: GARCH, Volatilitas, IHSG, Nilai Tukar ABSTRACT This study aims to examine the effect of the volatility of the return on the IDR-USD exchange rate toward  the volatility of stock market returns in the Indonesia Stock Exchange. From the data collection from 3 January 2012 until 29 September 2017 we obtained 1404 time series. Analyzing data, this study used  GARCH modeling and Granger Causality Test. The selected GARCH (1,1) modeling result shows that the volatility of exchange rate influences the volatility of Indonesian Composite Index.  Granger Causality test shows that the volatility of exchange rate and volatility of Indonesian Composite Index have two-way granger cause. This study indicates that exchange rate information can predict the condition of stock price index in capital market and movement of Indonesian Composite Index (ICI) can predict exchange rate movement in foreign exchange market. Appropriate predictions by investors will reduce the risk and increase the yield in investing if the money market and capital markets are fluctuating high. Keywords: GARCH, Volatility, ICI, Exchange Rate

scholarly journals Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

2015 ◽  
Vol 622 ◽  
pp. 012022
Author(s):  
W S Gayo ◽  
J D Urrutia ◽  
J M F Temple ◽  
J R D Sandoval ◽  
J E A Sanglay
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Exchange ◽  
Composite Index ◽  
Series Analysis

STATISTICAL PROPERTIES AND MULTIFRACTAL BEHAVIORS OF MARKET RETURNS BY ISING DYNAMIC SYSTEMS

2012 ◽  
Vol 23 (03) ◽  
pp. 1250023 ◽  
Author(s):  
WEN FANG ◽  
JUN WANG
Keyword(s):  
Time Series ◽  
Ising Model ◽  
Stock Exchange ◽  
Mean Field Theory ◽  
Composite Index ◽  
Interaction Strength ◽  
Statistical Properties ◽  
Fluctuation Analysis ◽  
Power Law Distribution ◽  
Multifractal Detrended Fluctuation Analysis

An interacting-agent model of speculative activity explaining price formation in financial markets is considered in the present paper, which based on the stochastic Ising model and the mean field theory. The model describes the interaction strength among the agents as well as an external field, and the corresponding random logarithmic price return process is investigated. According to the empirical research of the model, the time series formed by this Ising model exhibits the bursting typical of volatility clustering, the fat-tail phenomenon, the power-law distribution tails and the long-time memory. The statistical properties of the returns of Hushen 300 Index, Shanghai Stock Exchange (SSE) Composite Index and Shenzhen Stock Exchange (SZSE) Component Index are also studied for comparison between the real time series and the simulated ones. Further, the multifractal detrended fluctuation analysis is applied to investigate the time series returns simulated by Ising model have the distribution multifractality as well as the correlation multifractality.

scholarly journals Backpropagation Neural Networks Implementation for JKSE Forecasting

2019 ◽  
Vol 8 (4) ◽  
pp. 9902-9905
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Stock Exchange ◽  
Promising Result ◽  
Composite Index ◽  
Experimental Result ◽  
Market Change ◽  
Series Analysis ◽  
Backpropagation Neural Networks

Neural networks is a type of soft computing methods that widely has been used and implemented in many fields, including time series analysis. One of the goals of time series analysis is to predict future data value.In this study, we try to implement another approach using the backpropagation neural networks method to forecast the Jakarta Stock Exchange (JKSE) composite index data, which is one of the stock market change indicators in Indonesia.The study then is continued by calculating the accuracy and robustness levels of Backpropagation NN in forecasting JKSE data. The experimental result on the case taken shows an encouraging and promising result.

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