Stock Price Prediction based on Stock Big Data and Pattern Graph Analysis

Jeon, Songhee; Hong, Bonghee; Kim, Ju-Hyeong; Lee, Hyun-jik

doi:10.5220/0005876102230231

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…German Adhoc messages also had been used as input and for feature selection, chi square method has been used and SVM algorithm when 65% accuracy is obtained [9] . Stock prediction model using logistic regression was implemented considering feature index variables, whereby daily stock trading prediction with logistic regression out performs other method like ANN prediction model [10] .…”

Section: Related Workmentioning

confidence: 99%

Digital Signal Processing for Predicting Stock Prices in Financial market

2020

JECET

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Financial market is quite dynamic and challenging in trading where investor will either make money or lose their entire life savings. Predictive logic and analysis tries to evaluate whether there is any similarity between the historical data and future tendency. Accurate prediction and analysis of huge amount of financial data facilitate the retrieval of significant information from large data, which is unknown. In this paper, an attempt is made for prediction of stock prices and transaction trend to understand its volatile nature and predict its behavior to gain profits.

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Section: Related Workmentioning

confidence: 99%

Digital Signal Processing for Predicting Stock Prices in Financial market

2020

JECET

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“…According to the literature review, famous methods or techniques for flood forecasting include machine learning [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] and hydrological techniques [7][8][9][10][11][12][13][14]. However, most involved studies revealed the frequently used technique for forecasting works by big data, i.e., machine learning, rather than hydrological techniques [33][34][35][36][37][38][39] because hydrological models are mostly used with fixed factors that are less flexible to be increased as needed. As a result, forecasting accuracy is different in each particular area due to different spatial contexts.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results of flood forecasting revealed close accuracy between hydrology and machine learning [30][31][32]. Even so, when considering their suitability for big data processing, it was found that machine learning techniques are widely used for forecasting in other aspects, i.e., stock price forecasting [33,34], health and medical analytics [35], weather forecasting, and forecasting other kinds of disasters [37][38][39]. When using big data and machine learning for flood forecasting, traditional data is still mostly relied on.…”

Section: Introductionmentioning

confidence: 99%

Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data

Puttinaovarat

Horkaew

2019

Int. J. Interact. Mob. Technol.

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Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.

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“…There are various methods for pattern matching such as Euclidean distance, Dynamic Time Warping (DTW)( [4]), Edit Distance with Real Penalty (ERP)( [14]), Longest Common Subsequence (LCSS)( [39]) and Edit Distance on Real Sequence (EDR)( [15]). Although we use a hierarchical clustering algorithm based on Euclidean distance by reason of finding similar patterns quickly and simultaneously in previous paper ( [23]), because the Euclidean distance method does not accurately identify trading price trends due to a limitation that i th point in one sequence should be calculated with the i th point in the other, we find similar patterns using DTW method that accurately identify trading price trends than anything else ( [3,16]).…”

Section: Pattern Retrieve Using Dynamic Time Warpingmentioning

confidence: 99%

Pattern graph tracking-based stock price prediction using big data

Jeon

Hong

Chang

2018

Future Generation Computer Systems

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Stock price prediction is the most difficult field due to irregularity. However, because stock price is sometimes showing similar patterns and is determined by a variety of factors, our new idea is to find similar patterns in historical stock data to achieve daily stock price with high prediction accuracy and potential rules selecting main factors that have significant effect on the price among all factors simultaneously. The goal of our paper is to suggest a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use a Dynamic Time Warping algorithm to find patterns with the most closely similar situation adjacent to a current pattern. Second, we select the determinant that are most influenced by the stock price using feature selection based on Stepwise Regression Analysis. Moreover, we generate an artificial neural network model with selected features as training data for predicting the best stock price. Finally, we use Jaro-Winkler distance with Symbolic Aggregate approXimation (SAX) as prediction accuracy measure to verify our model.

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Stock Price Prediction based on Stock Big Data and Pattern Graph Analysis

Cited by 9 publications

References 9 publications

Digital Signal Processing for Predicting Stock Prices in Financial market

Digital Signal Processing for Predicting Stock Prices in Financial market

Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data

Pattern graph tracking-based stock price prediction using big data

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