Stock Price Prediction Based on Morphological Similarity Clustering and Hierarchical Temporal Memory

Stock Price Prediction Based on Morphological Similarity Clustering and Hierarchical Temporal Memory

Predicting stock prices through historical data is a hot research topic. Stock price data is considered to be a typical time series. Recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent units (GRU) have been commonly employed to handle this type of data. However, most s...

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Bibliographic Details
Main Authors: Xingqi Wang, Kai Yang, Tailian Liu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Machine learning
kmeans
morphological similarity distance
hierarchical temporal memory
stock prediction
Online Access:https://ieeexplore.ieee.org/document/9420698/

Internet

https://ieeexplore.ieee.org/document/9420698/

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