Bitcoin prediction and parameter analysis based on LSTM

Ding Wang

doi:10.62051/mttynh47

Authors

Ding Wang

DOI:

https://doi.org/10.62051/mttynh47

Keywords:

Long Short-Term Memory; Stock price prediction; Deep learning.

Abstract

Stock price prediction is currently a research focus in the financial field, especially in blockchain research. The central focus of this research is to forecast Bitcoin's closing price through the integration of deep learning techniques, specifically employing Long Short-Term Memory (LSTM). This study takes into account that Bitcoin is a mainstream virtual currency, and predicting its future price can help investors make better judgments in trading. The goal of this exploration is to identify the most favorable parameter combinations and function prediction applications, ultimately obtaining the most accurate prediction results. The research process includes dataset selection, data processing, model construction, and training. Then adjust and improve the parameters used in the model, and record the process. Finally, test the model and output the test results. And model testing and result output. At the end of the experiment, the effects of different optimizers and parameters on the training results were compared, and the optimal combination was found. The model's predictive accuracy was evaluated through the examination of test data. This study can provide valuable references for researchers and firms.

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