Kati8/ICLR_2023_2025 huggingface dataset:
authors: Antony Krymski
authors: Tom Davison
reviews: 
Comments: This paper proposes to use contrastive predictive coding as a way of automating feature engineering for financial time series forecast. The authors integrated a contrastive representation learning technique with LSTM model and evaluated forecast on sharpe ratio metric.
Strengths: The authors considered using contrastive representation learning to automate the feature engineering process.
Weaknesses: - citation reference format is wrong
- there is no comparison with SOTA models or even vanilla transformers. there are only 11 references, authors should take a detailed look at the current landscape of literatures on financial time series forecasting
- the metric used are not standard time series forecasting metric such as MSE and MAE
- the experimental result is weak (only table 1)
- the presentation is poor, CPC is not explained in detail in related work. there are missing references in the paper. For example "In the plots below", there is no reference to which plot the authors are referring. there are many typos in the paper: normalise -> normalize, maximise -> maximize, and so on. 
- there is a strong lack of novelty, CPC is already proposed and this work seems to be a simple application of CPC on financial time series. 
- min-max data normalization may be problematic in financial time series because extreme values significantly affects the normalized data. Extreme values are highly prevalent in financial time series, but one extreme value could lead to 99% of the data compressed into a tiny range after min-max normalization.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: This work proposes to utilise an automated feature generation architecture, Contrastive Predictive Coding (CPC), to generate embeddings as input to improve the performance of downstream financial time series forecasting models.
Strengths: The study tackles a pertinent research issue with possible academic or practical implications, serving as a solid foundation for further investigation. The endeavor to implement automated feature generation architecture to generate embeddings as input to improve the performance of downstream financial time series forecasting models. demonstrates an engagement with the subject, albeit certain aspects want improvement.
Weaknesses: This paper has significant flaws in its writing, such as the absence of a proper literature review and problem formulation. Structurally, it resembles a technical report rather than a rigorous academic paper, lacking detailed descriptions of the methodology, adequate experiments to validate the model's effectiveness, and comprehensive information about the data used.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: This paper explores the use of Contrastive Predictive Coding (CPC) to enhance financial time series forecasting by generating automated feature embeddings that capture underlying market patterns without manual intervention. Given the limitations of traditional models in handling the noisy, stochastic nature of financial data, the authors benchmark CPC embeddings against both complex (LSTM) and simpler models (linear regression, persistence) on currency pairs like USDJPY, USDSGD, and EURGBP. Results demonstrate that CPC-enhanced models, especially linear regression, outperform traditional approaches, indicating that CPC can produce robust, generalizable features suitable for diverse currency pairs. Additionally, the study finds that CPC-based strategies yield higher Sharpe ratios than a buy-and-hold approach, suggesting CPC’s potential in developing adaptive trading strategies across financial markets.
Strengths: 1. While CPC itself is not new, this paper attempts to apply it in a challenging, stochastic domain—financial time series—which traditionally poses difficulties for predictive models due to noise and volatility. This application may contribute to the field by assessing CPC’s generalizability to non-deterministic data, even if further methodological enhancements are needed.
2. The paper evaluates CPC embeddings not just on a single currency but across multiple currency pairs (USDJPY, USDSGD, EURGBP), demonstrating an effort to test its adaptability. This cross-domain testing shows that the embeddings have the potential to capture broader, generalizable patterns, although more rigorous experimentation would solidify this claim.
Weaknesses: 1. The paper offers minimal novelty, as it relies on Contrastive Predictive Coding (CPC), a technique introduced in 2018, without significant modification. The sole methodological addition is the custom negative sampling based on the Black-Scholes model, which does not sufficiently advance. More substantial methodological innovation would be required for a contribution of significance.
2. The experimental scope is limited and omits comparisons with current state-of-the-art models for time series forecasting, such as iTransformer, PatchTST, and DLinear. Additionally, the paper does not benchmark CPC against recent self-supervised learning models tailored for time series data, like TimeDRL, TS2Vec, or TS-TCC. This lack of comparative analysis weakens the results’ credibility and makes it difficult to assess the true efficacy of the proposed approach.
3. The paper suffers from organizational and formatting issues, which detract from clarity and professionalism. Notably, the references do not adhere to the ICLR format, and sections are structured in an unconventional manner. For instance, the Related Work section should be distinct, and the Methodology section includes experimental details that would be better placed in a separate Experiments section. Improved structure and adherence to standard formatting would enhance the paper's readability and alignment with conference expectations.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 1