Modeling and forecasting financial time series with ordered fuzzy candlesticks

Marszałek, Adam; Burczyński, Tadeusz

doi:10.1016/j.ins.2014.03.026

Cited by 69 publications

(25 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This aspect turned out to be useful in many real-world scenarios, such as representing trends in control processes, expressing diversity of opinions in social networks, modeling dynamics of financial data, and simulating brain functions [3,7,18,23].…”

Section: Interpretations Of Ofnsmentioning

confidence: 99%

Ordered Fuzzy Numbers: Definitions and Operations

Prokopowicz

Ślęzak

2017

Theory and Applications of Ordered Fuzzy Numbers

View full text Add to dashboard Cite

We outline basic notions and assumptions related to the Ordered Fuzzy Number (OFN) model. Definitions of mathematical operations, several interpretations of their results, as well as additional OFN parameters are presented. Some of them, such as inclination or order, are specific to OFNs, whereas others are equivalent to those present in the well-known convex fuzzy number model. An important aspect of this part is also a discussion of algebraic properties of the OFN model.

show abstract

Section: Interpretations Of Ofnsmentioning

confidence: 99%

Ordered Fuzzy Numbers: Definitions and Operations

Prokopowicz

Ślęzak

2017

Theory and Applications of Ordered Fuzzy Numbers

View full text Add to dashboard Cite

show abstract

“…In the vulnerable spots, where the data can be sniffed, a hacker is able to get only a portion of transmitted data. These data do not carry any clue as to what part of the original data they are [4,6,41].…”

Section: Multipath Tcp Secure Schedulermentioning

confidence: 99%

Fuzzy Control for Secure TCP Transfer

Apiecionek

2017

Theory and Applications of Ordered Fuzzy Numbers

View full text Add to dashboard Cite

This chapter presents the potential use of fuzzy observance implementation for detecting transmission problems that could appear in the near future. Using quick detection, appropriate action could be taken and the security and reliability of data transfer could be maintained at a high level. As a result the authors present a proposed solution for dividing a data stream between different data links and predicting transmission problems.Keywords TCP · Multipath TCP · Security · Fuzzy logic · Fuzzy observation IntroductionNowadays many networks require security such as data encryption and reliable transfer to the destination. Health care, rail, and power plant systems are some examples of such systems. When they are used an operator cannot lose a connection between his or her control and management applications and system sensors and actuating equipment. In such a system a lost connection could have tragic consequences. In the case of power plants this could also mean some huge disaster. That is why security regarding reliable transfer is very important. In the case of health systems it could be required for monitoring people but also for execution of medical operations through remote control of medical equipment such as a scalpel. Some problems with rail equipment could cause a train accident. There are also many other systems in which a lost connection would cause real damage. Such systems are called critical infrastructure. For this critical infrastructure more than one connection is usually prepared. For example, those systems can operate using cable and a 3G/4G/LTE connection concurrently. Unfortunately, in most cases, different kinds of used connections are not operated simultaneously. Systems switch between them when the operating connection collapses. When the systems switch connections they can lose some data.

show abstract

“…However, predicting stock prices is not an easy work, due to the complexity and chaotic dynamics of the markets and the many nondecidable, nonstationary stochastic variables involved [9]. Many researchers from different areas have studied the historical patterns of financial time series and have proposed various methods for forecasting stock prices.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

Zhou

Pan

et al. 2018

Mathematical Problems in Engineering

163

110

View full text Add to dashboard Cite

Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.

show abstract

Modeling and forecasting financial time series with ordered fuzzy candlesticks

Cited by 69 publications

References 5 publications

Ordered Fuzzy Numbers: Definitions and Operations

Ordered Fuzzy Numbers: Definitions and Operations

Fuzzy Control for Secure TCP Transfer

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

Contact Info

Product

Resources

About