DELPHYNE: A Pre-Trained Model for General and Financial Time Series

Aakriti Mittal; Achintya Gopal; Xueying Ding

REVIEW 4 major objections 6 minor 70 references

DELPHYNE: A Pre-Trained Model for General and Financial Time Series

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A time-series transformer pre-trained on general and financial data matches foundation and dedicated models after a few fine-tuning steps, and beats them on finance tasks.

desk verdict Negative-transfer study is the real contribution; the Monash aggregate is computed on a biased subset and the abstract overpromises. read the letter →

arxiv 2506.06288 v1 pith:PMAP5GHD submitted 2025-05-12 q-fin.ST cs.AIcs.LG

Xueying Ding , Aakriti Mittal , Achintya Gopal This is my paper · ORCID

classification q-fin.STcs.AIcs.LG

keywords time-seriesfoundationmodelsnegativetransferfine-tuningfinancialtimeseriesprobabilisticforecastingany-variateattentionnowcastingmixtureofStudent-tdistributions

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the value of a pre-trained time-series model lies in fine-tuning, not zero-shot transfer, and that a model pre-trained on general plus financial data can therefore serve both worlds. It documents a negative-transfer effect—mixing financial data into the pre-training corpus degrades zero-shot forecasts on general benchmarks, in synthetic experiments (GARCH versus wavelet generators) and in the full model alike—and proposes Delphyne, an encoder transformer with any-variate attention, a missing-data mask alongside the forecast mask, and a mixture of Student-$t$ output distribution. The paper claims that after a few fine-tuning steps Delphyne becomes competitive with existing foundation and full-shot models on public benchmarks, and that it beats them on financial tasks such as stock-return risk, intraday volume, and company-revenue nowcasting. If correct, this reframes what a time-series foundation model is for: not immediate zero-shot generalization, but fast adaptation from small amounts of task data.

What carries the argument

The central mechanism is an encoder transformer built to tolerate the messiness of financial series: all variates are flattened and given identity embeddings, series are right-padded, split into patches of size 32, and a missing-data mask runs alongside the forecast mask so that irregular sampling, multiple frequencies, and contemporaneous (nowcasting) inputs do not misalign the data. Attention is any-variate attention with rotary position embeddings, and the forecast is a mixture of Student-$t$ distributions. Around this architecture sits the paper's main conceptual mechanism: the demonstration and removal of negative transfer. Using synthetic GARCH-versus-wavelet models, a Bayesian MCMC posterior analysis, and the Delphyne-A versus Delphyne-L comparison, the paper argues that heterogeneous pre-training data degrades zero-shot forecasts, and that fine-tuning is what 'unlearns' that bias—making the fine-tuning protocol, not the zero-shot prior, the load-bearing part of the system.

What would settle it

Re-run the Monash fine-tuning evaluation with validation and hyperparameter selection confined to the training split of each dataset (or to a time-based split that never touches the test horizon), and check whether Delphyne-A-FT still posts an aggregate normalized MAE near 0.536 and still rivals MOIRAI; if the clean-protocol numbers degrade materially, the 'competitive after few fine-tuning steps' claim loses its support. A supporting check is to replicate the financial tasks on years after 2023, well beyond the end-2019 pre-training cutoff, to test whether fine-tuned finance gains persist under drift.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that pre-training a time-series transformer jointly on the public LOTSA corpus and on financial data—stocks, ETFs, currencies, commodities, and intraday bars—produces a model whose zero-shot performance on general tasks is worse than that of a model trained on LOTSA alone, yet whose fine-tuned performance matches or beats the best available baselines. Delphyne-A (LOTSA plus finance) shows higher zero-shot MAE than Delphyne-L (LOTSA only) on ETTh2 and on the Monash archive, but after fine-tuning the two converge, and Delphyne-A-FT posts the best numbers on the financial tasks: next-day stock-return risk likelihood (NLL 1.741), 78-step intraday bar-volume error (MSE 0.551), and nowcast MAE of 0.071 on company revenue growth, with quantile-coverage statistics close to optimal. The paper reads these results as evidence that the negative transfer induced by heterogeneous pre-training data is real and inherent—confirmed by GARCH-versus-wavelet synthetic models and a Bayesian MCMC posterior analysis—and that fine-tuning is the mechanism that 'unlearns' the pre-training bias.

Load-bearing premise

The load-bearing premise is the Monash fine-tuning protocol: validation for early stopping and hyperparameter selection is carved out of the same hold-out test horizon that later produces the reported scores, so if that protocol leaks test information, the fine-tuned gains on public benchmarks—including the aggregate 0.536 normalized MAE—are optimistic.

Editorial extensions

If this is right

A single pre-trained model covering general plus financial data can serve both worlds: after a short fine-tuning phase it reaches an aggregate 0.536 normalized MAE on the Monash archive and rivals the best foundation model on several datasets.
On financial tasks, fine-tuning delivers the wins: Delphyne beats the Student-$t$ GARCH baseline in next-day return likelihood, the fine-tuned Delphyne variants post the best errors on intraday bar volume and company-revenue nowcasting, and coverage statistics sit close to optimal.
Zero-shot results systematically understate a model trained on heterogeneous data, so evaluations of time-series foundation models should report fine-tuned performance; the negative-transfer penalty is a property of the data mixture, not a failure of the architecture.
Pre-training choices such as longer context lengths and lighter masking ratios improve downstream few-shot adaptation, so these hyperparameters should be selected with the fine-tuning regime in mind.
The same fine-tuned model also handles probabilistic forecasting (CRPS/MSIS) and anomaly detection, indicating that one foundation model can cover forecasting, uncertainty quantification, and monitoring after light adaptation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension the paper leaves open: give the finance-only model (Delphyne-F) a much larger fine-tuning budget on financial tasks and check whether the general-domain prior in Delphyne-A eventually becomes irrelevant or actively harmful, which would sharpen the question of which pre-training data actually pays for itself.
The missing-data mask suggests an imputation use the paper does not develop: evaluate Delphyne on genuinely irregular calendars (holidays, half-days, multi-frequency lags) where left-padding and zero-filling baselines should degrade, and measure whether the mask preserves forecast calibration.
Because the financial pre-training data stops at end-2019, the reported finance results cover 2020-2023; a natural stress test is whether the fine-tuned advantage survives rolling windows after 2023, when the pre-training distribution is older.
The negative-transfer diagnostic could be repurposed: a practitioner facing a new domain could read zero-shot degradation as a cheap signal of domain mismatch, and use its size to decide how much fine-tuning data to collect.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is an engineering paper with one genuinely interesting idea—negative transfer in pretrained time-series models, demonstrated with synthetic GARCH vs wavelet data—and a set of financial evaluations that are mostly clean. The Monash comparison, though, is not apples-to-apples, and the abstract overstates the zero-shot story.

What's good: the synthetic negative-transfer experiments (both the direct pretraining comparison and the Bayesian MCMC version) are the most novel part, and they make a real point: mixing finance and non-finance data hurts zero-shot performance, and fine-tuning recovers it. The finance tasks use temporally separated splits, and fine-tuned Delphyne-A beats MOIRAI, PatchTST, GARCH and the provider baseline on stock NLL, bars volume, and nowcasting. The ablations on context length, masking ratio, any-variate attention, and output distribution are useful and honestly reported; some are on synthetic data, but they inform design choices.

Where it gets soft. The headline Monash number in Fig. 6 compares Delphyne-A-FT's aggregate (0.536) to MOIRAI's 0.58, but they are not computed over the same datasets. Appendix B.1 and Table 17 show Delphyne has no fine-tuned entries for M1 Monthly, M3 Monthly, M3 Other, Tourism Quarterly, CIF 2016, Vehicle Trips, Carparts, and Hospital—datasets where its zero-shot is often much worse than MOIRAI (Sunspot 3.51 vs 0.08, Pedestrian Counts 52.99 vs 23.17). Dropping those can only help the aggregate, so the 'rivaling MOIRAI' claim rests on a matched-subset comparison that is never shown. That's the main flaw, and it's fixable: report the aggregate over the common subset. The reader's validation-leak concern is a misread; 'partition out the same forecast length as the test set' describes carving a validation set from the training split, not test leakage.

Also, the abstract says 'competitive performance ... with few fine-tuning steps,' but zero-shot CRPS on Electricity and Solar is far behind MOIRAI (0.159 vs 0.055; 0.905 vs 0.419), and fine-tuning on Solar actually makes it worse (1.306). The paper does admit these weaknesses in Sec 5.3, but the abstract doesn't reflect them. 'Few fine-tuning steps' is never quantified—early stopping is reported, not steps. And no code, weights, or proprietary data are released, so the trained model has to be taken on faith.

Verdict: the negative-transfer story and the finance results are worth refereeing. A serious referee should ask for the matched-subset Monash comparison and a more measured abstract. I'd send it to review, and I'd cite the negative-transfer synthetic study if I were working on time-series foundation models.

Referee Report

4 major / 6 minor

Summary. The paper introduces Delphyne, an encoder-only transformer pretrained on the LOTSA public corpus and on proprietary financial data, with any-variate attention, missing-data and forecast masks, and a mixture-of-Student-T output distribution. The authors document a negative transfer effect when financial and non-financial data are mixed, argue that fine-tuning is the remedy, and evaluate Delphyne on Monash short-term forecasting, long-horizon out-of-distribution forecasting, probabilistic forecasting, anomaly detection, and four financial tasks (stock risk, volatility, intraday bars, and company nowcasting). The central claim is that Delphyne is competitive with existing foundation and full-shot models after few fine-tuning steps on public benchmarks and superior on financial tasks.

Significance. If the claims are supported, the paper would make a meaningful contribution: it provides evidence that a time-series foundation model can incorporate proprietary financial data and recover from negative transfer through fine-tuning, and it gives a systematic study of design choices (masking, context length, variate handling, output distribution). Strengths of the paper include clean temporal separations in the financial experiments (pretraining data end in 2019 while evaluation windows start in 2021), consistent fine-tuning gains across most benchmarks, and a thorough ablation suite on synthetic data. The main weaknesses are that several headline comparisons are not computed on matched data subsets, the 'few fine-tuning steps' claim is not quantified, and some broad performance claims are contradicted by the paper's own tables. These issues are fixable but currently overstate the evidence.

major comments (4)

[Sec. 5.2, Tables 16-17] The headline Monash comparison is computed on unmatched dataset subsets. Table 17 has no fine-tuned entries for M1 Monthly, M3 Monthly, M3 Other, Tourism Quarterly, CIF 2016, Vehicle Trips, Carparts, and Hospital, so the Delphyne-A-FT aggregate 0.536 is a geometric mean over the remaining datasets, whereas the MOIRAI 0.58 aggregate in Table 16 covers all 28 datasets. On several of the excluded datasets Delphyne-A-ZS is much worse than MOIRAI (e.g., Sunspot 3.51 vs. 0.08; Pedestrian Counts 52.99 vs. 23.17), so dropping them can only improve Delphyne's aggregate. The sentence in Sec. 5.2 that fine-tuned Delphyne is 'rivaling the best model, MOIRAI' is not supported by the current tables; please report a matched-subset comparison and state the number of datasets in each aggregate.
[Abstract and Sec. 2] The claim of adaptation with 'few fine-tuning steps' and 'minimal iterations' is never quantified. Sec. 5.2 reports only that early stopping is used, Fig. 2's x-axis is pre-training steps, and no experiment reports the number of gradient updates, epochs, or wall-clock time for fine-tuning the main models. Without this information, the 'few fine-tuning steps' qualifier in the abstract is unsupported. Please report fine-tuning cost (e.g., steps or epochs) for the Monash, long-horizon, and financial experiments, or remove the qualifier.
[Sec. 5.4 and Table 12] The probabilistic-forecasting claim is broader than the evidence. After fine-tuning, Delphyne-A's CRPS on Electricity is 0.140, compared with 0.048-0.065 for full-shot baselines and 0.055 for MOIRAI zero-shot; on Solar the fine-tuned CRPS is 1.306, compared with 0.420-0.518 for baselines and 0.419 for MOIRAI zero-shot. The statement that fine-tuned Delphyne 'achieves the best results across various datasets' is inaccurate for these datasets. Please restrict the claim to the datasets where it holds, or add a discussion of why fine-tuning does not close the gap on Electricity and Solar.
[Sec. 5.1 and Appendix B.2] The financial evaluations are performed on proprietary data of the same types that appear in Delphyne's pretraining corpus (company consumer transactions, intraday bars, stock returns, and related fields). While the temporal split is clean (pretraining ends in 2019 and evaluation starts in 2021), the 'superior performances on various financial tasks' claim is an in-domain result; the comparison methods (MOIRAI, MOMENT, TTM, PatchTST) did not have access to this proprietary pretraining data, so the comparison is not controlled for pretraining data. Please state this limitation explicitly and, if possible, add an external financial benchmark that is not part of the pretraining corpus.

minor comments (6)

[Table 10 caption] The caption says 'MSE results' but the table reports MAE; please correct the caption.
[Appendix J, Table 29] MOIRAI is cited as (Wang et al., 2024a), but the correct reference is Woo et al. (2024); the current citation points to a graph negative-transfer paper.
[Table 1] The blank entries in Table 1 should be marked as not applicable rather than left empty, to avoid ambiguity about whether the measurement was not performed.
[Table 6] The sampling probabilities for LOTSA sum to approximately 85.1 and those for finance to approximately 14.2, not the stated 85 and 15; please reconcile the rounding.
[Sec. 5.2] The sentence 'We partition out the same forecast length as the test set, used as validation data for finetuning the model' can be misread as using test data for validation; please clarify that the validation set is carved from the training split.
[Fig. 2] The x-axis of Fig. 2 is described as training steps but the axis label is missing; please add a label and, if possible, indicate the fine-tuning step count on the same axis.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the Monash aggregate-subset mismatch is a benchmark fairness concern, not a circularity.

full rationale

The paper's chain of reasoning is empirical rather than derivational: the pretraining objective in Eq. (2) is a standard NLL forecasting loss, the negative-transfer demonstrations are synthetic-data experiments, and the general-benchmark evaluations use held-out test splits. No prediction or claimed result is defined in terms of its own input. The Monash validation split described in Sec. 5.2 partitions out a validation horizon from the training split, not the test set, so there is no leakage that would make the fine-tuned results forced by construction. The finance evaluations are partly in-distribution because Delphyne-A is pretrained on the same families of proprietary financial data (stocks, intraday bars, consumer transactions) that appear in the downstream tasks, but this limits external validity rather than making the result circular. The selection of the mixture-of-Student-t output distribution is an ablation-driven model choice, not a fitted parameter renamed as a prediction, and the self-citations (e.g., Gopal 2024) are contextual and not load-bearing. The comparison of Delphyne-A-FT's subset-based Monash aggregate (0.536 in Table 17) against MOIRAI's full-benchmark aggregate (0.58 in Table 16) is an unmatched-subset comparison that weakens the 'rivaling MOIRAI' claim, but it is a fairness and reporting issue, not a reduction of the result to its inputs. No circular step can be exhibited from the paper's equations or cited chain.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

Delphyne is a trained model, not a postulated physical entity; the masking mechanisms are methodological constructs, not entities requiring independent evidence. The free parameters listed are hand-chosen training and architecture constants that the central empirical claim depends on.

free parameters (5)

pretraining data mixture = 85% LOTSA / 15% financial
Chosen by the authors; determines how much financial signal enters pretraining and is central to the finance-versus-general tradeoff.
variate sampling beta-binomial = alpha=2, beta=5
Controls how many variates appear per training sample; affects multivariate and nowcasting behavior.
masking beta-binomial = alpha=5, beta=10 (mean about 30%)
Mask ratio is a tuned pretraining hyperparameter; ablations in Sec. 3.2 motivate moderate masking.
patch size = 32
Fixed patch length for tokenization; affects forecast resolution and context coverage.
context length = 512 x 32 timesteps
Truncation length used in pretraining; chosen to support long financial histories.

assumptions (5)

domain assumption Diverse pretraining on LOTSA plus proprietary financial data yields transferable time-series representations.
Foundational premise of the method; the paper's own negative-transfer results show cross-domain pretraining can hurt, so this assumption is not guaranteed.
domain assumption Negative transfer observed in synthetic wavelet and GARCH experiments transfers to real financial pretraining.
Sec. 2 generalizes from synthetic data and one ETTh2 checkpoint comparison to the full Delphyne design.
domain assumption Mixture of Student-T distributions is adequate as the output family for all downstream tasks.
Ablation in Sec. 3.4 uses stock NLL only; adequacy on other datasets is assumed.
ad hoc to paper The Monash test horizon can be used as a validation set for fine-tuning without biasing results.
Sec. 5.2 states the test set partition is used as validation; this is a protocol assumption that affects the general fine-tuning claim.
domain assumption Proprietary financial data cutoff at end-2019 prevents lookahead leakage into evaluations after 2020.
Sec. 4.1 states the cutoff; the paper provides no data lineage audit or external verification.

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Cite this review

Pith. "Pith review of DELPHYNE: A Pre-Trained Model for General and Financial Time Series." pith.science (2026). https://pith.science/paper/PMAP5GHD

@misc{pith2026250606288,
  author       = {Pith},
  title        = {Pith review of: DELPHYNE: A Pre-Trained Model for General and Financial Time Series},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PMAP5GHD}},
  note         = {Machine review of arXiv:2506.06288}
}

read the original abstract

Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data. Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains. However, across financial applications, existing time-series pre-trained models have not shown boosts in performance over simple finance benchmarks in both zero-shot and fine-tuning settings. This phenomenon occurs because of a i) lack of financial data within the pre-training stage, and ii) the negative transfer effect due to inherently different time-series patterns across domains. Furthermore, time-series data is continuous, noisy, and can be collected at varying frequencies and with varying lags across different variables, making this data more challenging to model than languages. To address the above problems, we introduce a Pre-trained MoDEL for FINance TimE-series (Delphyne). Delphyne achieves competitive performance to existing foundation and full-shot models with few fine-tuning steps on publicly available datasets, and also shows superior performances on various financial tasks.

Figures

Figures reproduced from arXiv: 2506.06288 by the authors.

**Figure 2.** MAE on ETTh2 across (average forecast length of {96, 192}) training. Finance data hurts zero-shot (ZS) performance of Delphyne-A on ETTh2, but finetune (FT) eventually “undoes” the effect. Given the negative transfer phenomenon, why do we need to build a pre-trained time-series model? The strength of a pre-trained timeseries model lies in its ability to quickly adapt to downstream tasks with finetuning on only a f… view at source ↗

**Figure 3.** Delphyne Overview 3.1 OVERALL ARCHITECTURE Following recent approaches (Woo et al., 2024; Goswami et al., 2024), we adopt a transformer encoder structure as the backbone of Delphyne [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Stock NLL performance on three different output distributions. We include both publicly available data and financial data in Delphyne’s training, allowing Delphyne to generalize well to daily time-series forecast tasks as well as financial time-series tasks. We give…

**Figure 6.** Figure 6: Aggregated geometric mean of normalized MAEs for Delphyne zero-shot (ZS) and finetuned(FT) models. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

**Figure 7.** Figure 7: Aggregated Adjusted F1 Score for DelphyneA vs. comparison baselines. We measure adjusted F1 score for the anomaly detection task, on 44 time-series datasets for the UCR anomaly detection archive, in comparison to popular full-shot models and foundation model MOMENT …

**Figure 8.** Figure 8: Visualization of fine-tuned forecasts from [PITH_FULL_IMAGE:figures/full_fig_p030_8.png]

**Figure 9.** Figure 9: Visualization of fine-tuned forecasts from Delphyne-A on Stock Variance dataset. Note that since sometimes the squared returns are very large, we clip the plot but not the data during training and evaluation. Stock 1 Stock 2 Stock 3 Stock 4 [PITH_FULL_IMAGE:figures/…

**Figure 11.** Figure 11: Visualization of fine-tuned forecasts from Delphyne-A on Nowcasting Company Revenue dataset. Series 1 Series 2 Series 3 Series 4 [PITH_FULL_IMAGE:figures/full_fig_p032_11.png]

**Figure 13.** Figure 13: Visualization of fine-tuned forecasts from MOMENT on Financial Bars dataset. Series 3 Series 2 Series 1 Series 4 [PITH_FULL_IMAGE:figures/full_fig_p033_13.png]

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1913

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