Some variation of COBRA in sequential learning setup

ArXiv ID: 2405.04539 “View on arXiv”

Authors: Unknown

Abstract

This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting.

Keywords: Multivariate Time Series Forecasting, Bayesian Optimization, Hyper-parameter Tuning, Cryptocurrency, Stock Index, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper introduces a novel ensemble method with advanced statistical formulations and detailed algorithmic descriptions, but also validates its claims with multiple real-world datasets, hyperparameter tuning comparisons, and clear performance metrics.

  flowchart TD
    A["Research Goal: Optimize Multivariate Time Series Forecasting"] --> B["Data Collection & Preprocessing"]
    B --> C["Three Datasets:<br>Stock Index, Crypto, Load Forecasting"]
    C --> D["Model Architecture:<br>COBRA Variations"]
    D --> E["Hyper-parameter Tuning:<br>BO vs. Grid Search"]
    E --> F["Computational Process:<br>Training & Forecasting"]
    F --> G["Key Findings:<br>1. Proposed methods outperform SOTA<br>2. BO > Grid Search<br>3. Preprocessing crucial"]