Artificial Bee Colony-Optimized LSTM for Bitcoin Price Prediction

A R T I C L E I N F O A B S T R A C T Article history: Received: 28 August, 2019 Accepted: 08 October, 2019 Online: 28 October, 2019 In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one model to another model that use LSTM will take a very long time, thus a method is needed for optimizing hyperparameter to get a model with a small RMSE. This research proposed Artificial Bee Colony (ABC) as a method in optimizing hyperparameter for models that use LSTM. ABC is a metaheuristic method that mimics the behavior of bee colonies in foraging. Optimized hyperparameter in this research consisted of sliding window size, number of LSTM units, dropout rate, regularizer, regularizer rate, optimizer and learning rate. In this research the proposed method called as ABC-LSTM. Bitcoin prices historical data was used as the dataset for evaluating the prediction of the models. The best ABC-LSTM model resulted best RMSE of 189.61 compared to model that use LSTM without optimization resulted best RMSE of 236.17. This result showed that ABC-LSTM model outperformed models that use LSTM without optimization.

[1]  Anshu Saxena,et al.  Predicting bitcoin price using lstm And Compare its predictability with arima model , 2018 .

[2]  Ruixuan Li,et al.  Stock Price Prediction Using Time Convolution Long Short-Term Memory Network , 2018, KSEM.

[3]  Andrea Baronchelli,et al.  Machine Learning the Cryptocurrency Market , 2018, Complex..

[4]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[5]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[6]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks , 2007, MDAI.

[7]  Amita Sharma,et al.  Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset , 2019 .

[8]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[9]  Adriano C. M. Pereira,et al.  Stock market's price movement prediction with LSTM neural networks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[10]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Dervis Karaboga,et al.  On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation , 2015, Inf. Sci..

[12]  Thomas M. Breuel,et al.  Benchmarking of LSTM Networks , 2015, ArXiv.

[13]  Jaewook Lee,et al.  An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information , 2018, IEEE Access.

[14]  Kyung-shik Shin,et al.  Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction , 2018, Sustainability.

[15]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[16]  James Higgins,et al.  Using ant colony optimization to optimize long short-term memory recurrent neural networks , 2018, GECCO.

[17]  Shouyang Wang,et al.  AdaBoost-LSTM Ensemble Learning for Financial Time Series Forecasting , 2018, ICCS.

[18]  Svetlana Borovkova,et al.  An Ensemble of LSTM Neural Networks for High-Frequency Stock Market Classification , 2018, Journal of Forecasting.

[19]  Zairi Ismael Rizman,et al.  NON-LINEAR AUTOREGRESSIVE WITH EXOGENEOUS INPUT (NARX) BITCOIN PRICE PREDICTION MODEL USING PSO-OPTIMIZED PARAMETERS AND MOVING AVERAGE TECHNICAL INDICATORS , 2018 .

[20]  Simon Caton,et al.  Predicting the Price of Bitcoin Using Machine Learning , 2018, 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP).

[21]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[22]  Marion O. Adebiyi,et al.  Stock Price Prediction using Neural Network with Hybridized Market Indicators , 2012 .

[23]  Mansour Sheikhan,et al.  Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data , 2012, Neural Computing and Applications.

[24]  Changha Hwang,et al.  Predicting Bitcoin Market Trend with Deep Learning Models , 2018 .

[25]  Thomas Fischer,et al.  Deep learning with long short-term memory networks for financial market predictions , 2017, Eur. J. Oper. Res..

[26]  Jang Huisu.  Predicting Bitcoin Prices by Using Rolling Window LSTM model , 2018 .

[27]  Yueting Chai,et al.  Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators , 2018, Neural Computation.