Multi-step Ahead Forecasting Using Cartesian Genetic Programming

Dzalbs, Ivars; Kalganova, Tatiana

doi:10.1007/978-3-319-67997-6_11

Cited by 1 publication

(4 citation statements)

References 12 publications

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“…Authors have previously developed a Cartesian Genetic Programming (CGP) algorithm, described in [27], where it was successfully used for time series multi-step ahead forecasting for various industrial time series. Thus, building on top of that knowledge, in this paper we apply CGP for financial time series forecast.…”

Section: Machine Learningmentioning

confidence: 99%

“… Changing the number of inputs in gate  Changing the gate function out of the function library  Changing the gate inputs  Changing the gate constant Furthermore, mutation of chromosome outputs is also allowed and the initial population is randomly generated. From previous work in [27], it was concluded that CGP offers competitive results compared to ANN and Support Vector Machines (SVM). The best configuration of CGP found in [27] is used in this work, see Table 2.…”

Section: Machine Learningmentioning

confidence: 99%

“…From previous work in [27], it was concluded that CGP offers competitive results compared to ANN and Support Vector Machines (SVM). The best configuration of CGP found in [27] is used in this work, see Table 2. Where i corresponds to a double value input in the Gate and const corresponds to the Gate constant as described in Fig 2. If the output of the gate is not a number or has overflowed, first input value of the gate is passed as the output.…”

Section: Machine Learningmentioning

confidence: 99%

“…Cartesian Genetic Programming parameters used for all test cases (except for Profit where custom fitness function is used). More detailed implementation of the architecture, please refer to previous work in[27].…”

mentioning

confidence: 99%

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Forecasting Price Movements in Betting Exchanges Using Cartesian Genetic Programming and ANN

Dzalbs

Kalganova

2018

Big Data Research

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Since the introduction of betting exchanges in 2000, there has been increased interest of ways to monetize on the new technology. Betting exchange markets are fairly similar to the financial markets in terms of their operation. Due to the lower market share and newer technology, there are very few tools available for automated trading for betting exchanges. The in-depth analysis of features available in commercial software demonstrates that there is no commercial software that natively supports machine learned strategy development. Furthermore, previously published academic software products are not publicly obtainable. Hence, this work concentrates on developing a full-stack solution from data capture, back-testing to automated Strategy Agent development for betting exchanges. Moreover, work also explores ways to forecast price movements within betting exchange using new machine learned trading strategies based on Artificial Neuron Networks (ANN) and Cartesian Genetic Programming (CGP). Automatically generated strategies can then be deployed on a server and require no human interaction. Data explored in this work were captured from 1st of January 2016 to 17th of May 2016 for all GB WIN Horse Racing markets (total of 204GB of data processing). Best found Strategy agent shows promising 83% Return on Investment (ROI) during simulated historical validation period of one month (15th of April 2016 to 16th of May 2016).

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%