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Title: The Sequentially-Learning-Based Algorithm with Multiple Output Nodes in Futures Forecast
Authors: Jheng, Yu-Jie
Tsai, Yu-Han
Tsaih, Rua-Huan
Contributors: 2019智慧企業資訊應用發展國際研討會
Keywords: ASLFN, Cramming, Softening, Future forecast
Date: 2019-06
Issue Date: 2019-07-17 15:05:00 (UTC+8)
Abstract: Investment is a way to increase assets. The types of investment are very diverse, including stocks, futures, funds and so on. Regardless of the form of investment, the consistent purpose of investors is to make a profit. Inevitably, these investment commodities are accompanied by risks, but different investment products have different risks and profitability. In the past, technical analysis usually used statistical methods to analyze the market. Although the results have reference value, the effect is limited. The reason is that whether it is stocks or futures, the trend of the two is highly nonlinear. AI has different characteristics that can break through the limitations of traditional analysis because it involves multidimensional explanatory variables and uses a large number of continuous long-term records to achieve more accurate prediction requirements and reasonable business insight. This study addresses this challenge through deriving a sequentially-learning-based algorithm for the single-hidden layer feed-forward neural networks (SLFN) with the binary input/output and making the technical justification. Within the training process, the amount of adopted hidden nodes is variable, and thus the SLFN becomes an adaptive single-hidden layer feed-forward neural networks (ASLFN).
Relation: 2019智慧企業資訊應用發展國際研討會
Data Type: conference
Appears in Collections:[2019智慧企業資訊應用發展國際研討會] 會議論文

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