27,955 research outputs found

    Time series forecasting for dynamic environments: The DyFor Genetic Program model

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    Copyright © 2007 IEEESeveral studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ldquodynamicrdquo GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GP's potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.Neal Wagner, Zbigniew Michalewicz, Moutaz Khouja, and Rob Roy McGrego

    Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

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    Azzali, I., Vanneschi, L., & Giacobini, M. (2020). Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12101 LNCS). Springer. https://doi.org/10.1007/978-3-030-44094-7_4 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.authorsversionpublishe

    Month ahead rainfall forecasting using gene expression programming

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    In the present study, gene expression programming (GEP) technique was used to develop one-month ahead monthly rainfall forecasting models in two meteorological stations located at a semi-arid region, Iran. GEP was trained and tested using total monthly rainfall (TMR) time series measured at the stations. Time lagged series of TMR samples having weak stationary state were used as inputs for the modeling. Performance of the best evolved models were compared with those of classic genetic programming (GP) and autoregressive state-space (ASS) approaches using coefficient of efficiency (R2) and root mean squared error measures. The results showed good performance (0.53<R2<0.56) for GEP models at testing period. In both stations, the best model evolved by GEP outperforms the GP and are significantly superior to the ASS models.No sponso

    Genetic Methods for Machine Learning Models: The Case of Financial Time Series Forecasting

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    Financial time series forecasting certainly is the case of a predictive modeling process with many challenges, mainly because the temporal structure of the data. Genetic programming, as a particular variation of genetic algorithms, can be used to as a feature engineering, importance and selection process all at once, it can provide highly interpretable symbolic features that have low colinearity among them and yet high correlation with a target variable. We present the use of such method for generating symbolic features from endogenous linear and autoregressive variables, along with a Multi-Layer Perceptron, to construct a binary predictor for the price of Continuous Future Contracts of the Usd/Mxn intra-day exchange rate. The proposition of this work is three fold, first is stated a variation to formulate the classical regression problem of forecasting a continuous value, into a classification problem of forecasting a discrete and binary value, also, in order to address the feature engineering step, the use of Genetic Programming is proposed for producing non linear variables highly correlated with a target and highly uncorrelated with each other, and finally, variations on the performance metrics and Folds of data to perform the training process are implemented. The results are presented for a Logistic regression and a Multi-Layer Perceptron applied to 6 years of historical prices for the UsdMxn Financial Future contract

    Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

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    This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

    Predicting the energy output of wind farms based on weather data: important variables and their correlation

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    Pre-print available at: http://arxiv.org/abs/1109.1922Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data. © 2012 Elsevier Ltd.Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagne
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