Image de couverture de livre

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

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Liu, Shun, Wu, Kexin, Jiang, Chufeng, Huang, Bin, Ma, Danqing
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Statistical Finance
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Multi-period Learning for Financial Time Series Forecasting
par: Zhang, Xu, et autres
Publié: (2025)

Beyond Visual Realism: Toward Reliable Financial Time Series Generation
par: Zhang, Fan, et autres
Publié: (2026)

An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting
par: Li, Chufeng, et autres
Publié: (2024)

Reasoning on Time-Series for Financial Technical Analysis
par: Koa, Kelvin J. L., et autres
Publié: (2025)

Machine Learning Methods for Pricing Financial Derivatives
par: Fan, Lei, et autres
Publié: (2024)

FNSPID: A Comprehensive Financial News Dataset in Time Series
par: Dong, Zihan, et autres
Publié: (2024)

Contrastive Learning of Asset Embeddings from Financial Time Series
par: Dolphin, Rian, et autres
Publié: (2024)

Towards Financially Inclusive Credit Products Through Financial Time Series Clustering
par: Bester, Tristan, et autres
Publié: (2024)

Bitcoin Forecasting with Classical Time Series Models on Prices and Volatility
par: Kareem, Anmar, et autres
Publié: (2025)

Improved Financial Forecasting via Quantum Machine Learning
par: Thakkar, Sohum, et autres
Publié: (2023)

Forecasting Intraday Volume in Equity Markets with Machine Learning
par: Cucuringu, Mihai, et autres
Publié: (2025)

Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data
par: Sadeghi, Agathe, et autres
Publié: (2023)

Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data
par: Xiao, Minheng
Publié: (2022)

Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses
par: Masoudi, Oday, et autres
Publié: (2025)

Test-Time Adaptation for Non-stationary Time Series: From Synthetic Regime Shifts to Financial Markets
par: Wu, Yurui, et autres
Publié: (2026)

Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data
par: Katz, Harrison
Publié: (2026)

Foundation Time-Series AI Model for Realized Volatility Forecasting
par: Goel, Anubha, et autres
Publié: (2025)

Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting
par: Tondapu, Narayan
Publié: (2024)

Memory, Roughness, and Information Persistence in Financial Markets: A Structural Approach to Volatility Forecasting
par: Deep, Akash, et autres
Publié: (2026)

SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series
par: Alouadi, Alexandre, et autres
Publié: (2026)

Exploring the Interpretability of Forecasting Models for Energy Balancing Market
par: Våle, Oskar, et autres
Publié: (2026)

A Controlled Comparison of Deep Learning Architectures for Multi-Horizon Financial Forecasting: Evidence from 918 Experiments
par: Saidd, Nabeel Ahmad
Publié: (2026)

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
par: Kim, Minwu, et autres
Publié: (2024)

Time-Varying Factor-Augmented Models for Volatility Forecasting
par: Zhang, Duo, et autres
Publié: (2025)

From fair price to fair volatility: Towards an Efficiency-Consistent Definition of Financial Risk
par: Bianchi, Sergio, et autres
Publié: (2025)

Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review
par: Ericson, Lars, et autres
Publié: (2024)

Volatility Forecasting in Global Financial Markets Using TimeMixer
par: Li, Alex
Publié: (2024)

Risk-Aware Financial Forecasting Enhanced by Machine Learning and Intuitionistic Fuzzy Multi-Criteria Decision-Making
par: Turgay, Safiye, et autres
Publié: (2025)

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
par: Rahat, Md. Yeasin, et autres
Publié: (2025)

Ultimate Forward Rate Prediction and its Application to Bond Yield Forecasting: A Machine Learning Perspective
par: Du, Jiawei, et autres
Publié: (2025)

Optimizing Time Series Forecasting: A Comparative Study of Adam and Nesterov Accelerated Gradient on LSTM and GRU networks Using Stock Market data
par: Makinde, Ahmad
Publié: (2024)

Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data
par: Gurgul, Vincent, et autres
Publié: (2023)

Leveraging Time Series Categorization and Temporal Fusion Transformers to Improve Cryptocurrency Price Forecasting
par: Peik, Arash, et autres
Publié: (2024)

Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock
par: Huang, Dengxin
Publié: (2023)

Trends and Reversion in Financial Markets on Time Scales from Minutes to Decades
par: Safari, Sara A., et autres
Publié: (2025)

Behavioral Machine Learning? Regularization and Forecast Bias
par: Frank, Murray Z., et autres
Publié: (2023)

Multivariate Financial Forecasting using the Chronos Time Series Foundation Models
par: Das, Sanjiv R, et autres
Publié: (2026)

Enhancing OHLC Data with Timing Features: A Machine Learning Evaluation
par: Tepelyan, Ruslan
Publié: (2025)

Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter
par: Tang, Siu Hin, et autres
Publié: (2023)

Financial sentiment analysis using FinBERT with application in predicting stock movement
par: Jiang, Tingsong, et autres
Publié: (2023)