Time series forecasting using massively parallel genetic programming

2004 ◽  
Author(s):  
S.E. Eklund
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Time Series Forecasting ◽  
Massively Parallel ◽  
Parallel Genetic Programming

scholarly journals A Study On Financial Time Series Forecasting And Symbolic Regression By Means Of A Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Mahsa Mostowfi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Genetic Programming ◽  
Probabilistic Model ◽  
Stock Price ◽  
Model Building ◽  
Symbolic Regression ◽  
Time Series Forecasting ◽  
Fusion Model ◽  
Regression Problems

This work proposes a hybrid algorithm called Probabilistic Incremental Cartesian Genetic Pro- gramming (PI-CGP), which integrates an Estimation of Distribution Algorithm (EDA) with Carte- sian Genetic Programming (CGP). PI-CGP uses a fixed-length problem representation and the algorithm constructs a probabilistic model of promising solutions. PI-CGP was evaluated on sym- bolic regression problems and next trading day stock price forecasting. On the symbolic regression problems PI-CGP did not outperform other approaches. The reason could be premature convergence and being trapped at a local minimum. However, PI-CGP was competitive at stock market forecasting. It was comparable to a fusion model employing a Hidden Markov Model (HMM). HMMs are extensively used for time-series forecasting. This result is promising considering the volatile nature of the stock market and that PI-CGP was not customized toward forecasting.

scholarly journals A Study On Financial Time Series Forecasting And Symbolic Regression By Means Of A Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Mahsa Mostowfi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Genetic Programming ◽  
Probabilistic Model ◽  
Stock Price ◽  
Model Building ◽  
Symbolic Regression ◽  
Time Series Forecasting ◽  
Fusion Model ◽  
Regression Problems

This work proposes a hybrid algorithm called Probabilistic Incremental Cartesian Genetic Pro- gramming (PI-CGP), which integrates an Estimation of Distribution Algorithm (EDA) with Carte- sian Genetic Programming (CGP). PI-CGP uses a fixed-length problem representation and the algorithm constructs a probabilistic model of promising solutions. PI-CGP was evaluated on sym- bolic regression problems and next trading day stock price forecasting. On the symbolic regression problems PI-CGP did not outperform other approaches. The reason could be premature convergence and being trapped at a local minimum. However, PI-CGP was competitive at stock market forecasting. It was comparable to a fusion model employing a Hidden Markov Model (HMM). HMMs are extensively used for time-series forecasting. This result is promising considering the volatile nature of the stock market and that PI-CGP was not customized toward forecasting.

Conceptual Developments in Genetic Programming for Time Series Forecasting

2015 ◽  
Vol 13 (8) ◽  
pp. 2728-2733 ◽  
Author(s):  
Carlos A. Martinez ◽  
Juan David Velasquez
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Time Series Forecasting

Hyper-Heuristics Using Genetic Programming to Time Series Forecasting

2018 ◽  
Author(s):  
Mariana Macedo ◽  
Carlos Henrique Macedo dos Santos ◽  
Eronita Maria Luizines Van Leijden ◽  
Joao Fausto Lorenzato de Oliveira ◽  
Fernando Buarque de Lima Neto ◽  
...  
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Time Series Forecasting

Time series forecasting with genetic programming

2016 ◽  
Vol 16 (1) ◽  
pp. 165-174 ◽  
Author(s):  
Mario Graff ◽  
Hugo Jair Escalante ◽  
Fernando Ornelas-Tellez ◽  
Eric S. Tellez
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Time Series Forecasting

scholarly journals Estimation Models Generation using Linear Genetic Programming

2009 ◽  
Vol 12 (3) ◽  
Author(s):  
Javier Martínez Canillas ◽  
Roberto Sánchez ◽  
Benjamín Barán
Keyword(s):  
Time Series ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Price Index ◽  
Consumer Price Index ◽  
Decision Rules ◽  
Time Series Forecasting ◽  
Linear Genetic Programming ◽  
Estimation Techniques ◽  
Estimation Models

The use of decision rules and estimation techniques is increasingly common for decision mak-ing. In recent years studies were conducted which applies Genetic Programming (GP) to obtainrules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is LinearGenetic Programming (LGP). LGP evolves instructions sequences of an imperative programminglanguage. This paper proposes estimation models generation for time series forecasting using LGP.The forecasting result for the Consumer Price Index (CPI) and the price of soybeans per ton showsthe potential of this new proposal.

scholarly journals Genetic Programming and Boosting Technique to Improve Time Series Forecasting

10.5772/9617 ◽  
2009 ◽  
Author(s):  
Luzia Vidal de Souza ◽  
Aurora T. R. Pozo ◽  
Anselmo C. ◽  
Joel M. C. da Ros
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Time Series Forecasting ◽  
Boosting Technique
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