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Title: 利用SVM模型判斷股票資料的隨機性成分
Using SVM Model to Classify the Random Components of Stock Data
Authors: 賴彥儒
Lai, Yan-Ru
Contributors: 曾正男
Tzeng, Jeng-Nan
Lai, Yan-Ru
Keywords: 預測模型
Forecasting model
Artificial Neural Network
Long­-short term memory,
Machine learning
Support vector machine
Date: 2021
Issue Date: 2021-08-04 15:40:23 (UTC+8)
Abstract: 該研究的目的是對股票的資料進行分類,以判斷在一段時間內的資料為函數行為或隨機噪音。為了訓練該模型什麼是函數行為和什麼是隨機噪音,我們用三種數學模型對股票資料進行了模擬,並利用訊號處理的技巧從真實股票資料中找出建立數學模型所需要的參數。 我們使用支持向量機(SVM)和具有長期短期記憶(LSTM)的深度學習模型進行分類。 我們的結果表明,由我們的模擬數據訓練的模型使用在實際數據的預測結果,在顯著水準alpha = 0.05下,我們的分類在統計上有顯著差異。
The purpose of the study was to classify the stock price as functional behavior or random noise in a fixed period. We simulated the data with three kinds of mathematics models to train the model what is functional behavior or random noise. The parameter of mathematics models calculated by the technique of signal processing, such as EEMD. We use the support vector machine(SVM) and the deep learning model with long short-term memory(LSTM) to classification. Our results showed that our model trained by our simulated data used prediction results based on actual data, which are statistically significantly different at the significance level alpha = 0.05 for our classification.
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