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arxiv: 2605.22387 · v1 · pith:CTDLCSKKnew · submitted 2026-05-21 · 💻 cs.LG · cs.CE

Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market

Houxuan Zhou , Sriram Prasad , Chenghao Huang , Jiajie Feng , Hao Wang This is my paper

Pith reviewed 2026-05-22 07:46 UTC · model grok-4.3

classification 💻 cs.LG cs.CE
keywords electricity price forecastingKolmogorov-Arnold NetworksXGBoosthybrid modelAustralian NEMtime series forecastingmachine learningrenewable energy
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The pith

A hybrid KAN and XGBoost model outperforms benchmarks for week-ahead electricity price forecasts in Australia's NEM.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a hybrid framework that merges Kolmogorov-Arnold Networks with XGBoost to forecast electricity prices one week ahead in the Australian National Electricity Market. It seeks to handle strong volatility, nonlinear dynamics, and price spikes by pairing KAN's global nonlinear representation with XGBoost's local robustness and tree-based strengths. Tests use real NEM data and an expanding-window strategy against baselines including SARIMAX, LSTM, standalone KAN, and XGBoost. The hybrid reduces mean absolute error by roughly 12 percent versus XGBoost and over 50 percent versus a naive baseline. Accurate forecasts matter for operational planning and risk management in markets with high renewable penetration and frequent extremes.

Core claim

The proposed hybrid KAN+XGBoost framework integrates the global nonlinear representation capability of KAN with the local robustness of XGBoost to capture both long-term dependencies and short-term price fluctuations. Experiments on real-world NEM data using an expanding window evaluation strategy show that the model outperforms SARIMAX, LSTM, standalone KAN, and XGBoost, reducing MAE by approximately 12% compared to XGBoost and by over 50% compared to a naive baseline.

What carries the argument

The hybrid KAN+XGBoost framework, which combines Kolmogorov-Arnold Networks for global nonlinear modeling with XGBoost for capturing local patterns and robustness in volatile time series.

Load-bearing premise

Performance estimates from expanding-window evaluation on historical NEM data will generalize to future market conditions without major regime shifts, data quality issues, or unmodeled external factors.

What would settle it

A large rise in forecast error when the model is tested on new NEM data from a period with major policy changes, extreme weather, or other unrepresented market shifts.

Figures

Figures reproduced from arXiv: 2605.22387 by Chenghao Huang, Hao Wang, Houxuan Zhou, Jiajie Feng, Sriram Prasad.

Figure 1
Figure 1. Figure 1: Overview of the proposed KAN+XGBoost hybrid forecasting frame [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of week-ahead electricity price forecasting performance in VIC over four evaluation weeks. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains challenging due to strong volatility, nonlinear dynamics, and frequent extreme price spikes. These challenges are particularly pronounced in the Australian National Electricity Market (NEM), where high renewable penetration further increases uncertainty. This paper investigates week-ahead electricity price forecasting and proposes a hybrid KAN+XGBoost framework that integrates Kolmogorov-Arnold Networks (KAN) with tree-based learning. The proposed approach combines the global nonlinear representation capability of KAN with the local robustness of XGBoost to capture both long-term dependencies and short-term price fluctuations. Experiments are conducted on real-world NEM data using an expanding window evaluation strategy. The results demonstrate that the proposed model outperforms benchmark methods, including SARIMAX, Long Short-Term Memory (LSTM), standalone KAN, and XGBoost, reducing MAE by approximately 12% compared to XGBoost and by over 50% compared to a naive baseline. The results suggest that hybrid learning strategies provide an effective and robust solution for electricity price forecasting in highly dynamic electricity markets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a hybrid KAN+XGBoost framework for week-ahead electricity price forecasting in Australia's NEM. It integrates Kolmogorov-Arnold Networks for global nonlinear representations with XGBoost for local robustness, evaluated via expanding-window protocol on real NEM data. The central claim is that the hybrid outperforms benchmarks (SARIMAX, LSTM, standalone KAN, XGBoost) with ~12% MAE reduction versus XGBoost and >50% versus naive baseline.

Significance. If the empirical gains prove reproducible with full architectural and statistical details, the work could advance hybrid models for volatile, high-renewable electricity markets by combining KAN's function approximation strengths with tree-based robustness. The expanding-window design on actual NEM data is a practical strength for temporal forecasting tasks.

major comments (3)
  1. [Abstract / Experimental Evaluation] Abstract and Experimental section: The central performance claim (~12% MAE improvement over XGBoost) is presented without architecture details for the KAN component (layers, grid size, spline order), hyperparameter search procedure for either model, exact data periods or split dates, or statistical significance tests (e.g., Diebold-Mariano) on the reported differences. These omissions are load-bearing for assessing whether the gains are robust or reproducible.
  2. [Experimental Evaluation] Experimental Evaluation: The expanding-window protocol tests chronological later periods but includes no change-point detection, OOD hold-out sets, or sensitivity analysis for structural breaks driven by renewable penetration or policy shifts. This directly affects the generalization assumption underlying the week-ahead forecasting claims in a market known for regime changes.
  3. [Proposed Framework] Model Description: The precise integration mechanism between KAN and XGBoost (e.g., whether KAN outputs serve as features for XGBoost, parallel ensemble, or sequential residual correction) is not specified with equations or pseudocode. This ambiguity prevents evaluation of how the hybrid captures both long-term dependencies and short-term spikes as claimed.
minor comments (2)
  1. [Related Work] Add explicit comparison to recent KAN variants or other hybrid time-series models in the related work section for better positioning.
  2. [Experimental Setup] Clarify the naive baseline definition and confirm that feature engineering does not introduce look-ahead bias across the forecast horizon.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed report. The comments identify important areas for improving reproducibility, clarity, and robustness of the empirical claims. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract / Experimental Evaluation] Abstract and Experimental section: The central performance claim (~12% MAE improvement over XGBoost) is presented without architecture details for the KAN component (layers, grid size, spline order), hyperparameter search procedure for either model, exact data periods or split dates, or statistical significance tests (e.g., Diebold-Mariano) on the reported differences. These omissions are load-bearing for assessing whether the gains are robust or reproducible.

    Authors: We agree that these details are necessary for reproducibility and independent verification. In the revised manuscript we will expand the Experimental Evaluation section to report the exact KAN architecture (number of layers, grid size, spline order), the hyperparameter search procedure (including the validation strategy used for both KAN and XGBoost), the precise data periods and expanding-window split dates, and the results of Diebold-Mariano tests on the MAE differences. These additions will be placed in the main text rather than only in supplementary material. revision: yes

  2. Referee: [Experimental Evaluation] Experimental Evaluation: The expanding-window protocol tests chronological later periods but includes no change-point detection, OOD hold-out sets, or sensitivity analysis for structural breaks driven by renewable penetration or policy shifts. This directly affects the generalization assumption underlying the week-ahead forecasting claims in a market known for regime changes.

    Authors: We acknowledge that electricity markets can experience regime shifts. The expanding-window design already uses all historical data up to each forecast origin, which partially mitigates non-stationarity. To further address the concern we will add (i) a sensitivity analysis that reports performance on distinct sub-periods and (ii) a brief discussion of potential structural breaks linked to renewable policy changes. Full change-point detection or dedicated OOD hold-out sets would require additional methodological development and are noted as a limitation for future work; we therefore treat this revision as partial. revision: partial

  3. Referee: [Proposed Framework] Model Description: The precise integration mechanism between KAN and XGBoost (e.g., whether KAN outputs serve as features for XGBoost, parallel ensemble, or sequential residual correction) is not specified with equations or pseudocode. This ambiguity prevents evaluation of how the hybrid captures both long-term dependencies and short-term spikes as claimed.

    Authors: We apologize for the lack of clarity. The hybrid architecture uses KAN to produce a global nonlinear representation that is then supplied as additional input features to the XGBoost model (i.e., KAN outputs augment the feature set for XGBoost). In the revised manuscript we will insert explicit equations describing the integration, together with pseudocode that shows the training and inference steps. This will make the claimed division of labor between long-term dependencies (KAN) and local spike robustness (XGBoost) unambiguous. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on direct model evaluation

full rationale

The paper proposes a hybrid KAN+XGBoost model for week-ahead electricity price forecasting and reports results from an expanding-window evaluation on historical NEM data. Performance metrics (MAE reductions vs. SARIMAX, LSTM, standalone KAN, and XGBoost) are obtained by training on past observations and testing on chronologically later periods; these are standard out-of-sample comparisons and do not reduce by construction to any fitted parameter or self-referential definition. No derivation chain, uniqueness theorem, ansatz smuggling, or renaming of known results is present in the described approach. The central claim therefore remains independent of the paper's own inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The performance claim depends on the empirical superiority of the hybrid integration, which in turn rests on many tunable hyperparameters and background assumptions about the representational strengths of each component model.

free parameters (1)
  • KAN and XGBoost hyperparameters
    Learning rates, network widths, tree depths, regularization terms and other tunable values are fitted to the training data to achieve the reported MAE reductions.
axioms (2)
  • domain assumption Kolmogorov-Arnold Networks provide effective global nonlinear function representation
    Invoked to justify KAN's role in capturing long-term dependencies; drawn from prior KAN literature.
  • domain assumption XGBoost supplies local robustness to short-term fluctuations and outliers
    Invoked to justify XGBoost's role in handling price spikes; drawn from known properties of gradient boosting.

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