Ensembled Direct Multi-Step forecasting methodology with comparison on macroeconomic and financial data
集成直接多步预测方法及宏观与金融数据比较AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要核心是宏观与金融时间序列的多步预测方法；股指水平预测不足以直接归入收益预测。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
集成直接多步预测方法及宏观与金融数据比较
- 研究问题
- 对 GDP、CPI、失业率和股指等序列，如何结合模型集成与直接多步预测，在控制重新训练成本的同时改善预测表现？
- 方法与思路
- 作者按表现、复杂度与资源需求选择并加权多类模型，在指定预测期采用直接多步预测，其他预测期采用迭代多步预测。
- 材料陈述的贡献
- 摘要称 EDMS 相对常规集成迭代预测，在所分析数据集上平均改善百分之三十三点三二，部分改善超过百分之六十。
- 阅读关注点
- 应核对改善所用指标、训练与测试窗口、权重估计是否避免信息泄漏，以及直接预测与迭代预测切换的具体期限。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未清楚说明改善指标、显著性检验或期限设置，所报比例仅属作者陈述。
原文摘要
Accurate forecasts of macroeconomic and financial data, such as GDP, CPI, unemployment rates, and stock indices, are crucial for the success of countries, businesses, and investors, resulting in a constant demand for reliable forecasting models. This research introduces a novel methodology for time series forecasting that combines Ensemble technique with a Direct Multi-Step (DMS) forecasting procedure. This Ensembled Direct Multi-Step (EDMS) approach not only leverages the strengths of both techniques but also capitalizes on their synergy. The ensemble models in the methodology were selected based on performance, complexity, and computational resource requirements, encompassing a full spectrum of model complexities, from simple Linear and Polynomial Regression to medium-complexity ETS and complex LSTM model. Models were weighted based on their performances. In the DMS procedure we limit retraining to one- and five- year/month forecasts for economic and financial data respectively. Standard Iterative Multi-Step (IMS) procedure is employed for other horizons, effectively reducing computational demands while maintaining satisfactory results. The proposed methodology is benchmarked against Ensemble technique conventionally applied to IMS-generated forecasts, utilizing several publicly available macroeconomic and financial datasets. Results demonstrate a significant performance improvement with EDMS methodology, averaging a 33.32% enhancement across the analysed datasets, and sometimes improvement reaching above 60%.