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Forecast of the Stock Market Price using Artificial Neural Network and Wavelet Transform
http://doi.org/10.5626/JOK.2019.46.12.1249
With advancements in technologies on machine learning and artificial neural network, various researches have attempted to predict the changes in the price of the stock market. The prediction accuracy has improved with adoption of new artificial neural network technologies that have been developed for image and voice signal processing. In the present work, the technical indices from KOSPI were decomposed for the prediction of index and movement direction of KOSPI into high-frequency part and low-frequency part using wavelet transform, then used to predict KOSPI independently by using artificial neural networks. For the final prediction, the prediction result of each frequency part was added. CNN, DPN, and LSTM were employed as artificial neural network; the performance of each model was compared and the efficiency of the wavelet transform of input variables was analyzed. CNN with 0.51% of MAPE for the index prediction and LSTM with 81.7% of accuracy for movement prediction showed the best performance among the three models. The efficiency of wavelet transform was confirmed with averaged 38% of the improved performance for the index prediction and averaged 25% of the improved performance for the movement prediction.
A Study on P2P Lending Deadline Prediction Model based on Machine Learning
http://doi.org/10.5626/JOK.2019.46.2.174
Recently, there has been an increase in P2P lending users, a product that supports investments through lending among individuals using online platforms. However, since P2P lending`s investors have to take financial risks, the investors may fail to investment due to the close of investment while they considering whether to invest or not. This paper predicts how long an investment product will take from a certain point to the close in order to provide deadline information for P2P loan investment products. To predicts the investment deadline, we have transforms into Timeseries data and Step data based on investment information on actual P2P products. The regression, classification, and time series prediction model were generated using machine learning algorithm. The results of the performance evaluation showed that in the Timeseries data-based model, the Multi-layer Perceptron regression model and the classification model showed the highest performance at 0.725 and 0.703 respectively. The Step data-based model was also the highest with the Multi-layer Perceptron regression model and the classification model at 0.782 and 0.651 respectively.
Visual Analytics for Abnormal Event detection using Seasonal-Trend Decomposition and Serial-Correlation
In this paper, we present a visual analytics system that uses serial- correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial- correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the ‘Gyeongju Mauna Resort collapse’ and the ‘Jindo-ferry sinking’.
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