Enhancing Cryptocurrency Price Prediction by Applying Temporal Fusion Transformers on Subseries

Key Points

Improved prediction accuracy is achieved by categorizing financial time series into subseries with similar behavior.
The proposed method uses temporal fusion transformers along with attention mechanisms to predict future values.
Data from other cryptocurrencies is combined to improve training sets for each subseries category, mitigating data limitations.
Increasing model precision by carefully balancing category numbers ensures complex models are properly trained.

Abstract

In this work, we categorize a financial time series into a number of subseries with similar behavior to increase prediction accuracy by learning the subseries category. We create a deep learning model for each category based on the attention mechanism to predict its next step. Due to the limited amount of cryptocurrency data for training models, if the number of categories increases, the amount of training data for each model will decrease, and some complex models will not be trained well due to the large number of parameters. To overcome this challenge, we propose to combine the time series data of other cryptocurrencies to increase the amount of data for each category, thus increasing the precision of the models corresponding to each category.

Enhancing Cryptocurrency Price Prediction by Applying Temporal Fusion Transformers on Subseries

Key Points

Abstract

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