Search Word(s) in Title, Keywords, Authors, and Abstract: LSTM

Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network
Do-Hyung Kwon, Ju-Bong Kim, Ju-Sung Heo, Chan-Myung Kim and Youn-Hee Han
Page: 694~706, Vol. 15, No.3, 2019
10.3745/JIPS.03.0120
Keywords: Classification, Gradient Boosting, Long Short-Term Memory, Time Series Analysis
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In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.


Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction
Yeonguk Yu and Yoon-Joong Kim
Page: 1231~1242, Vol. 15, No.5, 2019
10.3745/JIPS.02.0121
Keywords: Attention Mechanism, LSTM, Stock Index Prediction, Two-Dimensional Attention
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This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2DALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.


DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos
Yeongtaek Song and Incheol Kim
Page: 150~161, Vol. 14, No.1, 2018
10.3745/JIPS.04.0059
Keywords: Activity Detection, Bi-directional LSTM, Deep Neural Networks, Untrimmed Video
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We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.


CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data
Kerang Cao, Hangyung Kim, Chulhyun Hwang and Hoekyung Jung
Page: 1508~1520, Vol. 14, No.6, 2018
10.3745/JIPS.02.0104
Keywords: Big Data, CNN, Correlation Analysis, Deep-Learning, LSTM
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In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.