Kati8/ICLR_2023_2025 huggingface dataset:
authors: Edward Selig
authors: Paul Alexander Bilokon
reviews: 
Comments: This paper introduces a new set of models - the Regularised Jump Models, developed to enhance both regime identification and feature selection within the Jump Model framework for financial time series analysis.
Strengths: The Regularised Jump Models adapt the Regularised K-means approach to the Jump Model framework and are designed to improve interpretability and accuracy over the Standard and Sparse Jump Models.
Weaknesses: 1. Although I am quite familiar with regime-switching, identification, and financial markets, the logical structure of this paper is challenging to follow. The introduction directly presents model definitions and the authors’ proposed improvements without first providing an overview of related work, the motivation, or the current state of research. This structure makes it difficult for readers to understand the unique contributions of this work and distinguish them from existing models. A clearer, more organized flow would greatly enhance readability and clarify the authors’ contributions.

2. The paper lacks a detailed motivation explaining why Regularised K-means, specifically, is optimal for adapting into the Jump Model framework. A comparison of Regularised K-means with alternative approaches could clarify why it is most suitable in this context.

3.  The paper briefly mentions Hidden Markov Models (HMM) and other regime-switching models but does not provide a thorough comparison, such as the latest model - RHINE: A Regime-Switching Model with Nonlinear Representation for Discovering and Forecasting Regimes in Financial Markets (SIAM SDM2024). Besides, although feature selection is central to the model, there is limited discussion on alternative feature selection methods in time series analysis.

4. The formulas (such as those in Eq. 1.6) lack detailed explanations for how each component, including the penalty terms $ P(\mu) $, specifically enhances feature selection and regime accuracy. 

5. The experimental part does not demonstrate how regime identification or interpretability is achieved. Additionally, there are no actual experimental results presented in the main text, yet the pseudo-code of the algorithms takes up several pages.

6. Appendix A is left blank, and the purpose of Proposition B.1 in Appendix B is unclear—is it merely meant to illustrate the classic partitioning principle of K-means? This is a well-known concept in machine learning, and furthermore, the authors’ so-called “proof” is missing.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: The paper proposed a Regularised Jump Model, an extension of the existing models based on the Jump Model Framework.This class of models aims to accurately identify different regimes of financial markets in particular through improved feature selection.  The proposed method relies on regularized K-means algorithm to achieve regularization. Several different penalty terms are considered for regularization (L0, Lasso, Ridge, Group-Lasso) and three different Jump Models (Stadard, Sparse, Regularized – the latter proposed in this paper) are compared in a simulation study.
Strengths: The authors identify a relevant problem in Jump Models for financial time series. With more development, this work could offer useful insights into this type of models and new ways to improve model regularization.
Weaknesses: - There is very limited information on related work; 
- The structure of the paper is not always clear; 
- The adaptations to the existing model are somewhat minor, and intuition isn't always provided on how the proposed methodology;
- There is no real-world data application/comparison; 
- The proposed model does not always outperform existing models (standard jump model and sparse jump model) and no recommendation is given on which one should be chosen in which situations (same holds for the different penalties in the proposed approach);
Recommendation: 1
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 1
reviews: 
Comments: Proposing algorithms for jump models while also utilizing feature selection.
Strengths: - Inclusion of multiple penalty terms in the proposed method.

-
Weaknesses: - Many typos throughout the manuscript.

- Math notations not defined, such as $\bar{\mu}$ in (1.2).

- Many details of the algorithms are missing.

- Real data application is missing with lowers the practical importance of the proposed methodologies.

- It seems that the total number of states K is assumed to be known. This is a restrictive assumption, especially in real data applications.
Recommendation: 3
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 1