Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems

Lukoševičiūtė, Kristina; Ragulskis, Minvydas

doi:10.1016/j.neucom.2010.02.014

Cited by 50 publications

(22 citation statements)

References 27 publications

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“…Selecting optimal among a large number of potential combinations is thus a search and optimization problem. Previous research has shown that GA algorithms can be effectively used to find a near-optimal set of time lags [47,48]. GA provides solution to this problem by an evolutionary process inspired by the mechanisms of natural selection and …”

Section: Ga-enhanced Lstm-rnn Modelmentioning

confidence: 99%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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Abstract:Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan's electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

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Section: Ga-enhanced Lstm-rnn Modelmentioning

confidence: 99%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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“…The full sort method could be used for the identification of the optimal set of time delays-only if m, L, and n are not very large and computational resources are not strictly limited. Otherwise, soft computing techniques could be exploited for the determination of the near-optimal set time delays [30]. Let us consider MackeyGlass time series [35] the discrete numerical solution to chaotic Mackey-Glass delay differential equation that reads…”

Section: Computational Experiments: Optimal Embedding Ofmentioning

confidence: 99%

Synchronization Measure Based on a Geometric Approach to Attractor Embedding Using Finite Observation Windows

et al. 2018

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A simple and effective algorithm for the identification of optimal time delays based on the geometrical properties of the embedded attractor is presented in this paper. A time series synchronization measure based on optimal time delays is derived. The approach is based on the comparison of optimal time delay sequences that are computed for segments of the considered time series. The proposed technique is validated using coupled chaotic Rössler systems.

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“…Once determined d and the lags, the ANN is tuned in a second stage. In Lukoseviciute and Ragulskis (2010) the evolutionary selection of lags is divided into two stages: first, the optimal dimension of the reconstructed phase space is determined by the false-nearest-neighbor algorithm and then a near-optimal set of time lags is found with a genetic algorithm for a fuzzy inference system.…”

Section: Lags Selectionmentioning

confidence: 99%

Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

et al. 2011

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This paper introduces Lags COevolving with Rbfns (L-Co-R), a coevolutionary method developed to face time-series forecasting problems. L-Co-R simultaneously evolves the model that provides the forecasted values and the set of time lags the model must use in the prediction process. Coevolution takes place by means of two populations that evolve at the same time, cooperating between them; the first population is composed of radial basis function neural networks; the second one contains the individuals representing the sets of lags. Thus, the final solution provided by the method comprises both the neural net and the set of lags that better approximate the time series. The method has been tested across 34 different time series datasets, and the results compared to 6 different methods referenced in literature, and with respect to 4 different error measures. The results show that L-Co-R outperforms the rest of methods, as the statistical analysis carried out indicates.

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Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems

Cited by 50 publications

References 27 publications

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Synchronization Measure Based on a Geometric Approach to Attractor Embedding Using Finite Observation Windows

Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

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