Forecasting financial time-series using data mining models: A simulation study

2020 ◽  
Vol 51 ◽  
pp. 101072 ◽  
Author(s):  
Imad Bou-Hamad ◽  
Ibrahim Jamali
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Simulation Study ◽  
Financial Time Series ◽  
Financial Time ◽  
Using Data

Data mining model for multimedia financial time series using information entropy

2020 ◽  
Vol 39 (4) ◽  
pp. 5339-5345
Author(s):  
Han He ◽  
Yuanyuan Hong ◽  
Weiwei Liu ◽  
Sung-A Kim
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Rapid Development ◽  
Development Trend ◽  
Series Data ◽  
Transaction Data ◽  
Relationship Analysis ◽  
Financial Time

At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 64-80
Author(s):  
Shiya Wang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Financial Time Series ◽  
Special Kind ◽  
Future Trend ◽  
Financial Time ◽  
Series Analysis ◽  
Depth Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

Financial Data Mining Using Flexible ICA-GARCH Models

2010 ◽  
pp. 255-272
Author(s):  
Philip L.H. Yu ◽  
Edmond H.C. Wu ◽  
W.K. Li
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Risk Management ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Independent Component ◽  
Garch Models ◽  
Financial Time

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.

Financial Time Series Data Mining

2011 ◽  
pp. 883-889
Author(s):  
Indranil Bose
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Trend Analysis ◽  
Prediction Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Future Trend ◽  
Series Data ◽  
Time Series Data Mining ◽  
Financial Time

Movement of stocks in the financial market is a typical example of financial time series data. It is generally believed that past performance of a stock can indicate its future trend and so stock trend analysis is a popular activity in the financial community. In this chapter, we will explore the unique characteristics of financial time series data mining. Financial time series analysis came into being recently. Though the world’s first stock exchange was established in the 18th century, stock trend analysis began only in the late 20th century. According to Tay et al. (2003) analysis of financial time series has been formally addressed only since 1980s. It is believed that financial time series data can speak for itself. By analyzing the data, one can understand the volatility, seasonal effects, liquidity, and price response and hence predict the movement of a stock. For example, the continuous downward movement of the S&P index during a short period of time allows investors to anticipate that majority of stocks will go down in immediate future. On the other hand, a sharp increase in interest rate makes investors speculate that a decrease in overall bond price will occur. Such conclusions can only be drawn after a detailed analysis of the historic stock data. There are many charts and figures related to stock index movements, change of exchange rates, and variations of bond prices, which can be encountered everyday. An example of such a financial time series data is shown in Figure 1. It is generally believed that through data analysis, analysts can exploit the temporal dependencies both in the deterministic (regression) and the stochastic (error) components of a model and can come up with better prediction models for future stock prices (Congdon, 2003).

USING WAVELETS TO COMPARE TIME SERIES PATTERNS

2005 ◽  
Vol 03 (04) ◽  
pp. 511-521 ◽  
Author(s):  
ELIZABETH ANN MAHARAJ
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Financial Time Series ◽  
Wavelet Coefficients ◽  
Randomization Tests ◽  
Daubechies Wavelets ◽  
Financial Time ◽  
Different Types

In this paper, a procedure for comparing the patterns of time series using wavelets is developed. Randomization tests based on the ratio of the sum of squared wavelet coefficients of pairs of time series at different scales are used. A simulation study using pairs of different types of time series using the Haar and Daubechies wavelets is carried out. The results reveal that the tests perform fairly well at scales where there are a sufficient number of wavelet coefficients. The tests are applied to a set of financial time series.

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