Regime Learning and Prediction on Time-series Data

时间序列数据的机制学习和预测

• 批准号: 537461-2018
• 负责人: Wang, Shengrui
• 金额: $ 4.01万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699390
• 关键词: Regime; Learning; Prediction; Time; series

This project investigates regime identification and characterization, and regime switching prediction in time series data, with applications in financial data analysis. A regime corresponds to a dynamic structure such as volatility, trends, patterns, dependence between financial assets. Regime switch analysis aims to detect such structures and their duration, and to forecast whether and how the structure changes from one regime to another. It is a key issue in many fields including climate dynamics, environmental ecology, financial economics, energy, meteorology, medicine, even tourism. As examples, portfolio managers of hedge funds could benefit from understanding the implications of regime switches in a financial ecosystem since the prediction of upcoming structural changes allows them to optimize the investment decisions, i.e. maximizing profits and reducing risks. Understanding regime switches in electricity consumption helps an electricity company forecast loads and manage the production or purchase of electricity. This project aims to discover cross-sectional market dynamics by investigating a new approach to regime identification and RS prediction. By approaching the problem from a data mining perspective, we will build a novel non-parametric/semi-parametric framework that eases the discovery of dynamics and features in the financial ecosystem. Our approach includes several innovative components. We search for and explore sequential patterns and trajectories in feature spaces to characterize regimes. We assess effects of outliers on regime changes. We search for and explore trajectories at the regime space in order to enable the use of non-linear machine learning models for RS prediction. We seek to identify causality rules and relationships between financial assets and their effectiveness in predicting important financial events, to provide insights on regime formation and duration and to leverage these insights for portfolio management. We design and develop new prediction models by exploring survival analysis, deep learning and pattern features.

本项目研究时间序列数据中的状态识别和表征，以及状态转换预测，以及在金融数据分析中的应用。一种制度对应于一种动态结构，例如金融资产之间的波动性、趋势、模式和相关性。体制转换分析旨在检测这种结构及其持续时间，并预测这种结构是否以及如何从一个体制变化到另一个体制。它是气候动力学、环境生态学、金融经济学、能源、气象、医学甚至旅游等多个领域的一个关键问题。例如，对冲基金的投资组合经理可以从了解金融生态系统中制度转变的影响中受益，因为对即将到来的结构性变化的预测使他们能够优化投资决策，即实现利润最大化和降低风险。了解电力消费中的制度变化有助于电力公司预测负荷和管理电力生产或购买。 这个项目旨在通过研究一种新的制度识别和RS预测方法来发现横截面市场动态。通过从数据挖掘的角度来处理这个问题，我们将构建一个新的非参数/半参数框架，以便于发现金融生态系统中的动态和特征。我们的方法包括几个创新的组成部分。我们在特征空间中搜索和探索序列模式和轨迹来表征政权。我们评估异常值对政权更迭的影响。为了能够使用非线性机器学习模型进行RS预测，我们在区域空间中搜索和探索轨迹。我们试图确定金融资产及其在预测重要金融事件方面的有效性之间的因果关系规则和关系，提供关于制度形成和持续时间的见解，并利用这些见解进行投资组合管理。我们通过探索生存分析、深度学习和模式特征来设计和开发新的预测模型。