Leveraging Time Series Categorization and Temporal Fusion Transformers to Improve Cryptocurrency Price Forecasting

ArXiv ID: 2412.14529 “View on arXiv”

Authors: Unknown

Abstract

Organizing and managing cryptocurrency portfolios and decision-making on transactions is crucial in this market. Optimal selection of assets is one of the main challenges that requires accurate prediction of the price of cryptocurrencies. In this work, we categorize the financial time series into several similar subseries to increase prediction accuracy by learning each subseries category with similar behavior. For each category of the subseries, we create a deep learning model based on the attention mechanism to predict the next step of each subseries. 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, hence increasing the accuracy of the models corresponding to each category.

Keywords: Time Series Prediction, Attention Mechanism, Deep Learning, Portfolio Management, Clustering

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning concepts like Temporal Fusion Transformers and Markov chains for categorization, indicating high mathematical density. While it includes quantitative results from backtests on cryptocurrency data, the lack of explicit implementation details (e.g., code, hyperparameters) or extensive statistical validation slightly limits empirical readiness.

  flowchart TD
    A["Research Goal: Improve Cryptocurrency Price Forecasting"] --> B["Step 1: Time Series Categorization\n(Clustering subseries with similar behavior)"]
    B --> C["Step 2: Cross-Cryptocurrency Data Augmentation\n(Combine data from other cryptos to increase training set size)"]
    C --> D["Step 3: Model Training\n(TFT Attention-based models for each category)"]
    D --> E["Outcome 1: Overcame small dataset limitations"]
    D --> F["Outcome 2: Achieved improved prediction accuracy"]
    E --> G["Outcome 3: Enhanced Portfolio Management & Decision Making"]
    F --> G