# Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach

arXiv: https://arxiv.org/abs/2401.00534 · HTML: https://mlsanity.com/paper/2401.00534/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Built on by (most cited first, 10 of 10)

- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [Image Captioning in news report scenario](https://mlsanity.com/paper/2403.16209/) (2024)
- [There Was Never a Bottleneck in Concept Bottleneck Models](https://mlsanity.com/paper/2506.04877/) (2025)
- [TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding](https://mlsanity.com/paper/2501.07335/) (2025)
- [A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration](https://mlsanity.com/paper/2402.19095/) (2024)
- [iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network](https://mlsanity.com/paper/2504.16432/) (2025)
- [Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach](https://mlsanity.com/paper/2402.10991/) (2024)
- [Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency](https://mlsanity.com/paper/2509.17695/) (2025)
- [Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators](https://mlsanity.com/paper/2407.11786/) (2024)
- [DecoKAN: Interpretable Decomposition for Forecasting Cryptocurrency Market Dynamics](https://mlsanity.com/paper/2512.20028/) (2025)

## Abstract (arXiv)

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.