Designing Time-Series Models With Hypernetworks & Adversarial Portfolios

ArXiv ID: 2407.20352 “View on arXiv”

Authors: Unknown

Abstract

This article describes the methods that achieved 4th and 6th place in the forecasting and investment challenges, respectively, of the M6 competition, ultimately securing the 1st place in the overall duathlon ranking. In the forecasting challenge, we tested a novel meta-learning model that utilizes hypernetworks to design a parametric model tailored to a specific family of forecasting tasks. This approach allowed us to leverage similarities observed across individual forecasting tasks while also acknowledging potential heterogeneity in their data generating processes. The model’s training can be directly performed with backpropagation, eliminating the need for reliance on higher-order derivatives and is equivalent to a simultaneous search over the space of parametric functions and their optimal parameter values. The proposed model’s capabilities extend beyond M6, demonstrating superiority over state-of-the-art meta-learning methods in the sinusoidal regression task and outperforming conventional parametric models on time-series from the M4 competition. In the investment challenge, we adjusted portfolio weights to induce greater or smaller correlation between our submission and that of other participants, depending on the current ranking, aiming to maximize the probability of achieving a good rank.

Keywords: Meta-learning, Hypernetworks, Time-series forecasting, Portfolio construction, Game theory (ranking), 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 hypernetwork-based meta-learning architecture with a formal mathematical treatment (e.g., parameterization, loss functions) while being heavily grounded in empirical results from the competitive M6, M4, and sinusoidal regression benchmarks, including code and backtest implementation.

  flowchart TD
    A["Research Goal: <br>Design Model for M6 <br>Forecasting & Investment <br>Challenges"] --> B1["Forecasting Challenge <br>Meta-Learning with Hypernetworks"]
    A --> B2["Investment Challenge <br>Game-Theoretic Portfolio"]

    B1 --> C1["Data: M4/M6 Time-Series"]
    B2 --> C2["Data: Participant Rankings"]

    C1 --> D1["Process: Simultaneous search <br>for parametric functions <br>& optimal parameters"]
    C2 --> D2["Process: Adjust weights to <br>induce correlation <br>for rank maximization"]

    D1 --> E1["Outcome: 4th Place<br>Forecasting"]
    D2 --> E2["Outcome: 6th Place<br>Investment"]

    E1 & E2 --> F["Final Outcome:<br>1st Place Overall<br>Duathlon Ranking"]