Long Short-Term Memory Neural Network Model for Time Series Forecasting: Case Study of Forecasting IHSG during Covid-19 Outbreak

Long Short-Term Memory (LSTM) is one of the developments from Recurrent Neural Network (RNN) architecture. In this paper, we use LSTM architecture for modeling and forecasting the Indonesian Composite Stock Price Index (IHSG) closing value data. We also compare the performance of the LSTM method with the ARIMA and the Radial Basis Function (RBF) Neural Network method. In the implementation, we use both R and Python open source software. For empirical study we use the data from January until August 2020 to see the performance of the considered methods during Covid-19 pandemic periods of time. From the analysis, we found that LSTM performs better than ARIMA, but outperformed by RBF for this data.

[1]  William W. S. Wei,et al.  Time series analysis - univariate and multivariate methods , 1989 .

[2]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[3]  Z. Reitermanová.  Data Splitting , 2010 .

[4]  J. Aitchison,et al.  Maximum-Likelihood Estimation of Parameters Subject to Restraints , 1958 .

[5]  Igor N. Aizenberg,et al.  Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production , 2014, Neurocomputing.

[6]  Wang Xiangxue,et al.  Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN , 2018, Arabian Journal for Science and Engineering.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[10]  Paulo S. G. de Mattos Neto,et al.  Correcting and combining time series forecasters , 2014, Neural Networks.

[11]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[12]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[13]  Igor N. Aizenberg,et al.  Multilayer Neural Network with Multi-Valued Neurons in Time Series Forecasting of Oil Production , 2014, MCPR.

[14]  DAVID G. KENDALL,et al.  Introduction to Mathematical Statistics , 1947, Nature.

[15]  Jürgen Schmidhuber,et al.  Learning Complex, Extended Sequences Using the Principle of History Compression , 1992, Neural Computation.

[16]  Marina Theodosiou,et al.  Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method , 2011, Neurocomputing.

[17]  Keith R. Matthews,et al.  Elementary Linear Algebra , 1998 .

[18]  S. D. Silvey,et al.  Maximum-Likelihood Estimation Procedures and Associated Tests of Significance , 1960 .

[19]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[20]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Neil Davey,et al.  Time Series Prediction and Neural Networks , 2001, J. Intell. Robotic Syst..

[23]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[24]  Alaa El. Sagheer,et al.  Time series forecasting of petroleum production using deep LSTM recurrent networks , 2019, Neurocomputing.

[25]  Driss Ouazar,et al.  Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting , 2009, Expert Syst. Appl..

[26]  Xiaohui Xie,et al.  Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks , 2016, AAAI.

[27]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[28]  Mohammad-Parsa Hosseini,et al.  Deep Learning Architectures , 2019, Deep Learning: Concepts and Architectures.

[29]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..