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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9420698/ |