CTBench: Cryptocurrency Time Series Generation Benchmark
Authors: Yihao Ang
Qiang Wang
Qiang Huang
Yifan Bao
Xinyu Xi
Anthony Kum Hoe Tung
Chen Jin
Zhiyong Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Time Series Generation
Crypto-centric Benchmark
Cryptocurrency Markets
Financial Evaluation Measure Suite
Primary area: datasets and benchmarks
TL;DR: In this work, we introduce CTBench, the first open time series generation benchmark tailored to cryptocurrency markets.

Abstract: Synthetic time series are vital for data augmentation, stress testing, and prototyping in quantitative finance. Yet in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, jeopardizing practical utility. Most prior work targets non-financial or traditional financial domains, focuses narrowly on classification and forecasting while neglecting crypto-specific complexities, and lacks critical financial evaluations, particularly for trading applications. To bridge these gaps, we introduce \textbf{CTBench}, the first \textbf{C}ryptocurrency \textbf{T}ime series generation \textbf{Bench}mark. It curates an open-source dataset of 452 tokens and evaluates models across 13 metrics spanning forecasting accuracy, rank fidelity, trading performance, risk assessment, and computational efficiency. A key innovation is a dual-task evaluation framework: the Predictive Utility measures how well synthetic data preserves temporal and cross-sectional patterns for forecasting, while the Statistical Arbitrage assesses whether reconstructed series support mean-reverting signals for trading. We systematically benchmark eight state-of-the-art models from five TSG families across four market regimes, revealing trade-offs between statistical quality and real-world profitability. Notably, CTBench provides ranking analysis and practical guidance for deploying TSG models in crypto analytics and trading applications. The source code is available at \url{https://github.com/MilleXi/CTBench/}.

Published as a conference paper at ICLR 2026
СТВЕEСH: CRYPTOCURRENCY TIME SERIES GENER- ATION BENCHMARK
Yihao Ang' Qiang Wang'
Qiang Huang?* Yifan Bao Xinyu Xi
Anthony K. H. Tung' Chen Jin Zhiyong Huang
'Department of Computer Science, National University of Singapore School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen) fyihao ang, yifan bao, atung, huangzy) nus . edu.sq (qwang, xinyu xi @uu. nus edu disjincenus edu. huangqian anggait.gth....e www.hi..edu.....
ABSTRACT
Synthetic time series are vital for data augmentation, stress testing, and prototyp- ing in quantitative finance. Yet in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Genera- tion (TSG) methods and benchmarks often fall short. jeopardizing practical util- ity. Most prior work targets non-financial or traditional financial domains, fo- cuses narrowly on classification and forecasting while neglecting crypto-specific complexities, and lacks critical financial evaluations, particularly for trading ap- plications. To bridge these gaps, we introduce CTBench, the first Cryptocurrency Time series generation Benehmark. It curates open-source dataset of 452 to- kens and evaluates models across 13 metrics spanning forecasting accuracy, rank fidelity, trading performance, risk assessment, and computational efficiency. A key innovation dual-task evaluation framework: the Predictive Utility mea- sures how well synthetic data preserves temporal and cross-sectional patterns for forecasting, while the Statistical Arbitrage assesses whether reconstructed series support mean-reverting signals for trading. We systematically benchmark eight state-of-the-an models from five TSG families across four market regimes, re- vealing trade-offs between statistical quality and real-world profitability. Notably. CTBench provides ranking analysis and practical guidance for deploying TSG models crypto analytics and trading applications. The source code available at htttps:://gitie thub. thub. com/ Mi leXi /tt//CTBenc///
INTRODUCTION
Time Series Generation (TSG) has become foundational for numerous downstream tasks, including data augmentation (Bao et al. 2024 Ramponi et al. 2018), anomaly detection (Ang et al. 2023b] Wang et al. 2021 privacy preservation (Jordon et al. 20188] Tian et al. 2024), and domain adap- lation (Cai et al. 2021 Li et al. 2022b). The core objective of TSG is synthesize sequences that preserve the temporal dependencies and structural characteristics of real-world data. Despite growing interest, the vast majority of existing TSG benchmarks and methods target domains such as healthcare, mobility, or sensor data (Ang et al. 2023a] 2024). Financial time series, which are inherently noisy, non-stationary, and adversarial, remain underexplored in the context of generative modeling. More importantly, even financial TSG efforts primarily focus on stock data (Yoon et al.) 2019 Wiese et al. 2020), often under simplifying assumptions that fail to generalize to emerging financial modalities. Consequently, the unique characteristics of modern financial markets, particu- larly in the digital asset space, are largely overlooked.
Cryptocurrencies, as a prominent subclass of financial time series with global market capital- ization exceeding $4 trillion as of May 2025 (Reuters) (025), introduce new modeling and eval- uation challenges. Unlike traditional financial instruments, crypto markets operate 24/7, lack in- trinsic valuation anchors, and exhibit extreme volatility driven by speculation, fragmented liquidity, "ooreesonniigg author.

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Published as a conference paper at ICLR 2026
and decentralized exchange in- frastructure. These proper- ties violate assumptions embed- ded existing financial bench- marks (Hu al.] [025 Wang itt al.) 2025; Qiu et al. 2024),
TimeWEE
GAN
RDDE
Bootshrop
Shate
Zan TrulingTee
CAse
which typically rely on regular trading hours, stable macroeco- nomic signals, broad station- arity assumptions.
While recent benchmarks for financial time series, such
8.8%% Theclig
Figure I: Aggregate rankings of eight TSG models on both tasks from 2021 to 2024: Predictive Utility (left) assesses fidelity and predictive signal quality, and Statistical Arbitrage (right) evalu- ates trading performance under realistic fee conditions. The re- sults reveal distinct trade-offss across fidelity, tradability, and ro- bustness, with no model uniformly dominating all measures.
as FinTSB (Hu al.] 2025) and FinTSBridge (Wang al.
2025), have advanced evaluation practices, they fall short in three critical aspects when applied to cryptocurrency settings:
Limited Domain Generality: Existing works (Ang et al. 2023a Hu et al. 2025) focus primarily on traditional assets such as equities and indices (e.g., SPX and CSI300) with lower volatility and restricted trading hours, offering minimal support for cryptocurrency data. They overlook the high-frequency, 24/7 dynamics of crypto markets.
Narrow Task Scope: Most financial time series benchmarks emphasize classification and fore- casting, neglecting generation and trading-centin centric tasks like arbitrage, which are vital for crypto- specific applications such as arbitrage and market-neutral strategies. Moreover, TSG methods in crypto contexts remain largely unexplored.
Lack of Crypto-Specifie Evaluation: Existing benchmarks underrepresent measures needed to assess real trading utility. While TSGBench focuses on statistical fidelity, and FSn introduces limited financial metrics, both rely assumptions from traditional markets, failing to consider continuous trading, heavy-tailed risk, and actionable signal quality unique to crypto assets.
To address these limitations, introduce CTBench, the first Cryptoeurrency Time series gen- eration Benchmark. It is an open-source benchmark for rigorous evaluation of TSG methods in cryptocurrency markets, with three key contributions:
Crypto-Centric Dataset. We provide a curated cryptocurrency dataset from major global ex- changes, processed via standardized pipeline with crypto-specific feature support. This ensures analysis-ready data reflecting the volatility and structural nuances of crypto markets.
Dual-Task Benchmarks. To operationalize the utility of synthetic data in real-world finance, CTBench introduces dual-task evaluation framework that jointly assesses predictive fidelity and tradability. The Predictive Utility task trains forecasters on synthetic data and tests them on real returns, while the Statistical Arbitrage task evaluates whether reconstructed residuals yield tradable mean-reverting signals.
Financial Metric Suite. CTBench introduces comprehensive evaluation suite over diverse trad- ing strategies spanning forecasting accuracy, rank-based measures, trading performance, and risk metrics, designed to reflect crypto-specific market realities.
We benchmark eight state-of-the-art TSG models and analyze trade-offs across fidelity, tradability. and robustness. Figure D shows aggregate rankings across tasks, with measures radially ranged and averaged over strategies and fee settings. No model dominates universally. highlighting distinct trade-offs between fidelity, tradability, and robustness, and underscoring CTBench's 's value for informed model selection in crypto trading contexts.
PRELIMINARIES
Let R E Rux denote the log-return matrix, where n is the number of tradable crypto-assets and 1 is the number of hourly return observations. At each time 1, the log-return vector across
all assets is r. [riss"" .n.c] E R", where each element is defined Ti,t log PL... with Pi.-1
2

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Crypto-Centric
Trading Strategles
Rew Datn Callection
Dara Prepeocesitg
Prafere Extraction
Crass- Sress-Scciinal Mementum
Aang-Osly Quanalle
Progortienal-
Predictive Lnilty
Statistiral Arbitragy
Financial Metrie Suite
TSG Model Zoo
Error-based
Trading Performance
Efficiency
GAN-base baN-based
VAE-based
MSE
MAE
CAGR
SR
MDD
Training
Informner
Quans-GAN
COSCI-GAN
TimeVAE
KoVAE
Rank--based
Risk Assesoment
Visualization
Diffasion: based
Flow based
Mixed
IR
VIR
Growth
Curve
Ranking
Floc
Diffisioe-TS
FIDE
Founer Flow
LS4
Figure 2: Overall architecture of CTBench. The framework unifies five modules: (1) crypto centric datasets, (2) dual-task evaluation (predictive utility and statistical arbitrage), (3) trading strategies, (4) comprehensive financial metrics, and (5) diverse TSG models into a unified benchmark pipeline.
Pi. the price of asset i at hour t. To mimic real-world backtesting, we employ a rolling-window protocol: Given training window size and test step we define split offsets O Each offset 7 yields a training and test split:
{w, w + 8, (k 1)8}, where k = --- (-w
R train [r.
rrl, R [1++1, [r+s]-
For each split, TSG model (r) is trained on R(( and evaluated two modes: (1) Generation Mode: sampling synthetic sequences from Gaussian noise, Rgen = (z) 2 NN,0 I); (2) Re-
construction Mode: reconstructing training and test set, R =g(") (R(T)) R = g (R(()
We further define a basic portfolio simulation setup: Starting with initial capital Vo >> O, the strategy allocates weights n7 [M,..> Mn.r. E R" at each hour t, where T/s, denotes the fraction invested in asset The portfolio value then evolves V, = Vi-1 x (m) (r), with hourly profit-and-loss given by AV; V V,-1- A summary of notations is provided Appendix [A] To maintain clarity and scope, CTBench restricts its benchmark design datasets, trading strategies, evaluation measures, and TSG models, as detailed Appendix B
3 СТВЕNCH
We present CTBench, the first benchmark specifically designed evaluate Time Series Generation (TSG) models in cryptocurrency markets (Figure
3.1 CRYPTO-CENTRIC DATASETS
Data Overview and Preprocessing. Our benchmark leverages historical hourly data from all USDT-denominated spot pairs on Binance (Binance Exchange] 2025b), spanning January 2020 to December 2024 and capturing diverse market regimes such bull runs, crashes, and consolidation phases. To ensure quality, we exclude assets with missing records and retain only USDT pairs, yielding 452 unique cryptocurrencies, robust foundation for TSG evaluation.
Formally, let denote the number of tradable crypto assets and (1+ 1) the number of hourly obser- vations. Each asset E {1,..- ,n) at time E {0, ... ,1] is represented by four standard fields:
Fi.t [Oi,e Hi,. L,A, C..l ER.
where O, H, L. and C are the Open, High, Low, and Close prices (quoted in USDT), respectively. Stacking across all assets yields the multi-asset OHLC data array: D [x,,c] [x+11x))
We focus primarily on Close prices and define hourly log-returns as: Titt log C, where
C..
t E (1,... ,(), giving the return matrix R Rexl

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Trainig buta Rews
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Trining Duta Row.
Ingut
Texi Datu Rust
Traising Phone
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Training
Trading Phase
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(a) Predictive Utility task.
(b) Statistical Arbitrage task.
Figure 3: Architectures of dual-task benchmarks.
Feature Extraction. To capture key market dynamics, we extract features widely used in quan- titative trading, such Alpha101 101 factors (Kakushadze) 2016), Bollinger Bands, RSI, and moving averages (Sun et al. 2023; Zhu & Zhu, | et al. 2020 Tsai & Hsiao 20T0). These fea- tures encode signals such as momentum, reverseev,,,,n, and volatility. Applying the same pipeline to both real and synthetic data enables consistent evaluation of TSG models' ability to replicate the statistical and structural properties vital for downstream tasks. Formally, let + { be the feature set, where each Φ; R + Rnxi acts on the return matrix R. Applying & yields a feature tensor with shape Rxxx The dataset exhibits strong cross-sectional dispersion and cap-dependent volatility, motivating crypto-specific evaluation of predictive structure and cross-assee dynamics be- yond marginal similarity (see Appendix C.I).
3.2 DUAL- DUAL-TASK EVALUATION
To connect generation fidelity with financial utility, CTBenchh introduces dual-task evaluation as- sessing both predictive realism and tradable structure. As shown Figure these tasks measure whether synthetic data preserves useful forecasting signals (predictive utility) or enables the discov- ery of stationary, market-neutral alpha (statistical arbitrage). Details are Appendix C.2
Predictive Utility Task. This task tests whether synthetic data can train forecasters that generalize
TSG model generates synthetic returns Rgen gzz). 12).2 2 ~ (O,, I). Features 4 ((Rgen) are extracted to
real markets. As shown in Figure ((a), given training log-retun window R(-)
train a forecasting model f. instantiated as XGBoost (Chen & Guestrin] 2016) for its robustness to heterogeneous and noisy financial features (Vancsura et al. 2025 Liu et al. 2021 Yun et al.) 2021). The ablation in $4.4 shows that XGBoost best balances low prediction error and strong cross-scctional ranking fidelity, making it the most informative evaluator for trading utility.
The trained deployed on real test data Rest to generate signals for a dollar-neutral long-short portfolio, rebalanced hourly over month. This setup tests whether Rgen preserves predictive signals with measurable economic value. All components, the TSG model, features, and the forecaster, are modular, allowing extensibility across architectures.
Statistical Arbitrage Task. contrast to the generation- focusee task, this task evaluates whether TSG models reconstruct market structure and isolate tradable, mean-reverting residuals for sta- tistical arbitrage. As depicted in Figure ((b) model g is trained on real returns Rizán to produce reconstructions Rurain and the residuals Pat rs.e - Pi.i are assumed to follow Ornstein-Uhlenbeck (OU) processes (Uhlenbeck & Ornstein 1930). parameterized by estimated (pi, 02, σ.)) per asset.
On test data, residuals 61 mapped standardized s-scores Si,t (GL. 14) /a)/(a;// /20.), which drive trading decisions via thresholding (? = 2) and weight normalization. Portfolios are rebalanced hourly based on these signals. This task complements generation- -fccused evaluation by assessing the model's 's ability to reveal stationary, market-neutral alpha, thus bridging statistical fidelity and practical trading utility.
3.3 TRADING STRATEGIES
evaluating TSG models across diverse trading paradigms. Profitabil- ity and risk metrics (3.4) are computed uniformly for all backtests, enabling rigorous stress testing
CTBench is
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whether models capture genuine market structure rather than overfitting specific strategies. We eval- uate models under three canonical trading strategies commonly adopted in cryptocurrency markets:
Cross-Sectional Momentum (CSM): take long positions in the top decile of predicted assets while shorting the bottom decile, capturing relative momentum effects.
Long-Only Top-Quantile (LOTQ):: build an equal-weight portfolio of the top 20% of assets, reflecting long-biaset strategies often favored in practice.
Proportional Weighting (PW): allocate capital in proportion to predicted returns, directly trans- lating forecasts into position sizes.
This modular design supports alug-and-play integration of additional or proprietary strategies. Full details are provided in Appendix
3.4 FINANCIAL METRIC SUITE
Evaluating TSG models for financial applications requires more than mere statistical similarity, demands measuring whether synthetic data enables profitable and risk-aware trading. To this end, CTBench defines thirteen core metrics, grouped into six practitioner--elevant categories:
Error-based Evaluation: At the most fundamental level, do synthetic returns numerically semble real ones? Metrics include Mean Squared Error (MSE), which emphasizes volatility mismatches, and Mean Absolute Error (MAE). which is more robust outliers.
Rank-based Evaluation: Do synthetic returns preserve relative asset ordering? Information
Coefficient (IC) measures rank correlation, and Information Ratio (IR) evaluates its stability.
Trading Performance: Does synthetic data yield actionable, profitable signals? Statistical accu- racy does not guarantee financial profitability. We therefore use Compound Annual Growth Rate (CAGR) for long-term growth and Sharpe Ratio (SR) for return-to-risk balance.
models capture fat tails and downside risks? We compute Max- imum Drawdown (MDD) for worst-case loss, Value at Risk (VaR) 95% confidence, and Ex- pected Shortfall (ES) for tail risk beyond VaR.
Risk Assessment Metrics:
Efficiency: Can models support real-time deployment? We track Training Time and Inference Time to assess adaptability in fast-moving crypto markets.
Visualization: Do results exhibit contextual realism? We report Simulated Growth Curves for a $10,000 investment and cross-sectional Ranking Plots across market regimes to illustrate interpretability and contextual realism.
Together, these metrics ensure balanced evaluation of fidelity, utility, and practicality, Full defini- tions Appendix C.4
3.5 TSG MODEL Zoo
Generative models for time series aim to capture temporal dependencies and structural patterns, with backbones spanning GANs, VAEs, diffusion models, flow models, and mixed-type models (Ang et al. 2023a Nikitin et al. 2023) (see Table Yet, nearly half of prior TSG works do not evaluate in financial contexts, and those that do typically focus on traditional markets such as equities or macroeconomic data, leaving gap for cryptocurrency applications. To close this gap, CTBench
evaluates eight state-of-the-art models spanning five families:
GANs: Quant-GAN (Wiese et al. 2020 and COSCI-GAN (Seyli et al. 2022). ) applied only in
forecasting tasks since GANs do not natively support reconstruction (Goodfellow et al., (202)).
VAEs: VAm (Desai al. 202T and Ko VAE NNamann al. 2024b), which extend varia-
tional autoencoders for temporal dynamics.
Diffusion Models: Diffusion-Tss (Yuan Qiao 2024) and FIDE Galib et al. 2024), leveraging denoising-based generative processes for time series.
Flow-based Models: Fourier-Flow (Alaa et al. 2021 employing invertible transformations for
likelihood-batt generation.
Mixed-type Models: LS4 (Zhou et al. 2023 designed to unify strengths across multiple gener- ative paradigms.
These models are selected for their generative fidelity and practical relevance to financial down-
stream tasks. Further details in Appendix C.5
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Quart GAN
CO63-GA
Trrewie
Kove
FDC
Faureniio
LS4
ARMANGARCH
Soomingg
24
Figure 4: Annual forecasting performance of TSG models on the Predictive Utility task.
OUAESAAN
COECHGAN
TrreWe
So/RE
Dilauss
PDB
LS4
(AG
-10
3.
2001
2524
2525
2004
2221
5822
2003
2004
2001
2002
205
2004
Figure 5: Annual trading performance of TSG models on the Predictive Utility task.
EXPERIMENTS
We evaluate CTBench 452 USDT trading pairs using walk-forward setup: each cycle trains 500 days and tests on 30 days (Predictive Utility) or 15 days (Statistical Arbitrage), with retraining before every window. To isolate generator quality, transaction fees are zero by default; for Statistical Arbitrage, we also report results under a 0.03% fee reflecting typical exchange costs Zhang et al. 2023); Winkel & Härdle (2023); Binance Exchange (2025a). We benchmark eight TSG models across five architectural families and include two classical baselines widely used in quantitative fi- nance: ARMA-GARCH (Engle 1982), evaluated on both tasks, and a Bootstrap generator (Rubin) 1981), used only for Predictive Utility. All models follow recommended stable hyperparameters and are scored using CTBench's 's full financial metric suite (details Appendix D
4.1 PREDICTIVE UTILITY TASK
year wise forecasting and trading performance from 2021 2024. The blue dashed line denotes the baseline using real data (without TSG), whose consistently strong returns validate the effectiveness of our feature-extraction pipeline ($3.1)
Figures 4 and Sjeport report
Annual Predictive Utility Analysis. Across all four market regimes, predictive accuracy and fi- nancial profitability often diverge, underscoring the difficulty of converting statistical fidelity into tradable signals. In 2021 (Bull Market), Diffusion-TS achieves the best forecasting error yet fails convert into returns, while ViE and COSCI-GAN demonstrate strong risk-adjusted per- formance by balancing denoising and alpha amplification. In 2022 (High Volatility), TimeVA remains resilient, and COSCI-GAL benefits from dispersion, whereas Diffusion- TS struggles with frequent reversals. During 2023 (Consolidation). Fourier-Flow outperforms in iisk-adjstted metrics due to preueency-preserving structure, while trend-reliant models degrade. By 2024 (Low-Signal / Mean Reversion), Most models face diminishing signal strength; only Time retains marginal profitability. Classical baselines ARMA-GARCH and Bootstrap maintain mid-tic performance: solid risk control but limited forecasting capability. Overall, results show that synthetic data must align with market structure and strategy design, not just minimize reconstruction error.
Ranking Analysis. Radar-plot rankings in Figure & three consistent patterns: (1) Diffusion- TS achieves the strongest forecasting scores but performs poorly in trading, illustrating a common gap between statistical fidelity and economic usefulness. (2) Time and COSCI-GAN show

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Grer CAA
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(b) 2022.
(c) 2023.
(d) 2024.
Figure Annual rankings of TSG models on the Predictive Utility task.
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Founer
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CAGRINT
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Figure 7: Annual performance of TSG models the Statistical Arbitrage task.
regime-dependent strengths: VAE excels in stable or mean-reverting markets due to its regu- larization, while COSCI-GAN benefits from volatile, directional regimes where amplified variance enhances trend signals. (3) Fourier-Flow maintains steady mid-to-high rankings across categories, positioning as robust, all-weather choice for risk-managed deployment.
These trends reinforce core insight: low reconstruction prediction error does not ensure trud- ing success. Over-regularization can suppress Hipha-bearing variance, while models that preserve structural noise VAF, COSCI-GAN) produce more actionable signals. Effective model choice must consider market regime and strategy alignment.
Equity Curve Dynamics. Figure 14 presents log-scaled equity curves from 2021-2024 for each TSG model across three trading strategies, highlighting how model inductive biases shape cumula- tive returns. COSCI-GAN consistently captures directional gains, while VAE and Fourier-Flow provide smoother but moderate returns. Diffusion- models underperform due to volatility sup- pression, and LS$ remains conservatively flat. The ARMA-GARCH, baseline similarly yields largely flat. The Bootstrap generator typically sits the upper middle of the pack, with equity curves that track the top 3-5 models. Full results and visualizations are Appendix E.]
4.2 STATISTICAL ARBITRAGE TASK
Figure reports annualized trading and risk metrics under both zero-fee and realistic-fee conditions. The blue dashed line represents Principal Component Analysis (PCA) baseline trained on Rumins reflecting standard statistiall-rrbiraag bascline used reference for TSG evaluation.
Annual Performance Analysis. All models exhibit reduced profitability under transaction costs, with the impact tied to turnover. Among TSG models, KoVAE excels in volatile regimes with high but mean-rewt reanrreevnting swings, whereas LS4 achieves strong returns with stable drawdowns, particu- larly in 2023. Even after fees, both maintain leading positions, underscoring the value of regime adaptability and cost-aware design. TimeVAE and Diffusion-TS form a second tier, trading off peak returns for smoother, fee-resilient SR, though their exposure tail risk, especially in 2021 and

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TimeWEE
Difusion-TS
FIDE
Fourler Flow
cas com
con
(a) 2021.
(b) 2022.
(c) 2023.
(d) 2024.
Figure 8: Annual rankings of TSG models on the Statistical Arbitrage task.
2024, limits overall efficiency. FIDE consistently yields negligible or negative returns but achieves the lowest VaR, ES, and MDD, suggesting over-regularized residuals that suppress tradable variance. Fourier-Flow similarly underperforms, failing capture mean-reverting structure despite effective noise smoothing. highlighting the limitations of exact-likelihood flow models in arbitrage-centric tasks. ARMA-GARCH delivers the top CAGR across 2021 to 2023, but consistently exhibits the weakest tail-risk profile, with the worst VaR and ES among all models.
Ranking Analysis. Figure & visualizes annual model rankings via radar plots, revealing how TSG models balance return, risk, and stability across regimes. KoVAE and LS4 form polygons that strongly protrude along CAGR and Sharpe but collapse along risk dimensions, indicating high re- turns paired with elevated drawdown and tail exposure, especially in turbulent years. FIDE shows the opposite pattern: tight risk control but consistently weak returns, reflecting a cupital-preserving yet alpha- litite design. Time VAE and Diffusion-TS produce more rounded, balanced shapes with neither dominant peaks nor severe failures, suggesting steady, regime-agnostic robustness.
Introducing trading fees compresses the rank distances: high-tumover models (e.g.. g.. KoVAE) lose several Sharpe positions, whereas smoother, lower- twerrtt models (e.g., TAEE Diffusion-TS) retain most of their ranking. This highlights smoother residual signals naturally incur lower costs and therefore achieve better fee-adjusted per- formance. Finally, the year-to-year evolution of polygon shapes reveals regime sensitivity: LS4 expands sharply on CAGR in 2023 but suffers high drawdowns in 2022, while KoVAE excels in volatile periods yet underperforms in calmer markets. These dynamics emphasize that model selee- tion must consider both regime characteristics and operational constraints.
important practical insight: models that generate
Equity Curve Dynamics. Fig- ure [5] illustrates fee-adjusted equity curves starting at $10,000
Quant-GAN
TImeVAE
Difuuion-TS
Fourler-Flow
COSCI-GAN
LS4
hour
with 0.03% trading fees. LS4
and KoVAE deliver sustained growth, while TimeVAE plateaus in later regimes. Diffusion-TS is stable but low- return; FIDE and Fourier-Flow
Time
1
Training
Inference
Figure 9: Training and inference time of TSG models.
underperform due to overly smoothed residuals. Remarkably, ARMA-GARCH exhibits moderate gains through early 2023 but experiences substantial growth afterward, ultimately becoming the hop-performing model by 2024-2025. Full results and visualizations are detailed in Appendix E.2
4.3 EFFICIENCY
We compare training and inference times for all TSG models in Figure VAE-based models are the most efficient: Tim trains in under a minute and achieves sub-second inference, making ideal for real-time applications, low-latency backtesting, and rapid retraining in fast-moving markets. GAN-based models offer moderate efficiency: COSCI-GAN maintains balanced runtime, while Quant-GAN is significantly more expensive to train with corresponding speed advantage at inference. Diffusion-busse models are the slowest: Diffusion-TS incurs the longest runtime due to iterative denoising, and FIDE provides only modest improvements. Despite their strong fidelity
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GuMl-GA COSC-GAN
TimAAZ
IMAA
Drtusoo-T5
FD2
Fouren-Fow
LS4
(a) XGBoost: strongest overall evaluator, with low errors and high rank correlation.
(b) Linear: weak predictive power and near-zero rank correlation.
MAC
cc) Random Forest: lower errors but poor cross-sectional ranking ability.
MAE
3.
2021
2032
2003
2024
2004
2002
200
2034
(d) MLP: flexible yet unstable, yielding high errors across regimes.
Transformer: captures long-range patterns but fails to produce stable rankings.
Figure 10: Ablation on the choice of forecasting model the Predictive Utility task. We compare five forecasters: XGBoost, Linear Regression, Random Forest, MLP, and Transformer, to assess whether mSG-model rankings remain stable across evaluators.
and risk-return profiles, these models are best suited for offline compute-rich settings. Flow- based and mixed-type models occupy the middle ground, offering reasonable efficiency and reliable likelihood calibration, though their latency limits real-time use. Overall, VAE-based models and LS4 emerge as the most practical choices for latency-sensitive or resource-constraing deployment, whereas diffusion-base models are better reserved for offline or batch-generation pipelines.
4.4 ABLATION STUDY ON FORECASTING MODELS
crucial component of the Predictive Utility task is the choice of forecasting model used assess the quality of synthetic data. In CTBench, we adopt XGBoost (Chen & Guestrin, 2016) as the default forecaster due strong empirical performance in quantitative finance and robustness to heterogencous, noisy features, properties particularly well aligned with crypto markets (Vancsural ctt al. 2025 Liu et al., 2021] Yun et al. (2021).
To validate this design choice, conduct an ablation study comparing five forecasting architec- tures: XGBoost, Linear Regression (Hilt & Seegrist, 1977). Random Forest (Breiman 2001). Multi-Layer Perceptron (MLP) (Hinton 1990). and Transformer-ber
sequence model /Vawwani
ctt al. 2017). Each forecaster is trained on features extracted from identical sets of synthetic data, ensuring that performance differences arise solely from the forecasting backhone rather than the

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Table 1: Scenario-based recommendations for selecting TSG models in cryptocurrency markets.
Scenario
Recommendation
Rationale
Trend-following Direc-
COSCI-GAN amplifies trend and dispersion; KoVAF offers al- pha with higher drawdlows
COSCI-GAN, KoVAE
tional Markets
Mean-reverting
Range-
TimeVAF, Fourier-Flow, Diffusion-TS
Time VAR Fourier-Flow provide halance; Diffusion-TS pre- rank order
bound Regimes
Fco-scnsitive
Low-
VAE, Diffusion-TS
Smooth residuals, stable Sharpe under transaction costs
tumover Settings
Risk Tolerance Portfolio
KoVAE, LS4, VAE Diffusion-TS, FIDE
KoVAE/ maximize returns with risk; VAEE Diffusiion TS balance Sharpe and drawdown; FIDE is defensive
Design
Fust retraining and low-lutency inference; diffusion models bet- ter suited for offline use
Deployment Efficieney
VAE, LS4
underlying TSG model. Evaluation follows the same walk-forward protocol as the main benchmark and uses both error-based metrics (MSE, MAE) and rank-based metrics (IC, IR).
Figure 10 summarizes the results. Linear Regression and MLP consistently exhibit high MSE and MÄE, indicating poor point-wise prediction accuracy. Random Forest and Transformer re- duce point-wise errors but fail preserve cross-sectional ordering, both achieving near-zero IC/IR, suggesting that they primarily fit short-term fluctuations rather than tradable structure. In contrast, XGBoost achieves the strongest balance: low prediction error paired with robust rank correlations, making it significantly more sensitive to the differences across TSG models and more aligned with how trading strategies consume forecasts.
Overall, the ablation demonstrates that XGBoost offers the most discriminative and trading- relevant evaluation signal, supporting its use as the default forecaster in CTBench. It consistently reveals meaningful differences between TSG models while avoiding the instability or rank collapse seen in alternative forecasters.
4.5
RECOMMENDATIONS
Our findings reveal four-way trade-off among TSG model families: (1) VAE-based models ensure stable reconstruction but might under-react to fast-changing regimes. (2) GAN-based approaches extract trend alpha but suffer from volatility-induced instability. (3) Diffusion models handle regime clustering and fat tails well, but degrade under low signal regimes. (4) Flow-based models prioritize likelihood but offer limited utility, while mixed-type ones are efficient but inconsistent in risk-return.
Based on these findings, Table presents actionable guidelines for practitioners. These recom- mendations help align model choice with market conditions, strategic objectives, and operational constraints. Crucially, there is no one-size-fits-al TSG model for cryptocurrency markets. In- stead, practitioners should: (1) diagnose the prevailing market regime, alpha source, and system constraints; (2) select a model whose inductive bias strengthens the target structure while preserv- ing tradability; and (3) evaluate performance using task-metri pairs that best reflect production goals. By integrating dual-task design with comprchensive evaluation suite, CTBench provides the decision surface needed to navigate these choices effectively.
CONCLUSION AND FUTURE WORK
We present CTBench, the first benchmark explicitly designed for time series generation in cryp- tocurrency markets, combining a high-frequency token dataset, a dual-task evaluation framework, and a comprehensive suite of financial metrics to assess both statistical fidelity and practical via- bility. Empirical results reveal key trade-offs across TSG families and highlight strategy model behaviors, offering actionable insights for real-world deployment.
Looking ahead, we plan to enhance CTBench by incorporating more advanced forecasters in the Predictive Utility task and integrating richer residual modeling processes for the Statistical Arbitrage task. Future directions also include expanding to a broader set of tokens, incorporating exogenous signals such trading volume, and benchmarking more sophisticated generative architectures.
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ACKNOWLEDGMENTS
This research/project is supported by the National Natural Science Foundation of China (NSFC) under Grant No. U25B6003, the National Research Foundation, Singapore, under its Al Singa- pore Programme (AISG Award No: AISG3-RP-2022-029 and CyberSG R&D Programme (CRPO Award No: CRPO-GC5-NUS-OU6 and the Ministry of Education, Singapore, under its MOE AcRF TIER I Grant (TI 25 25IRES2517).
ETHICS STATEMENT
CTBench is developed improve the rigor, transparency, and practical relevance of TSG evaluation in financial contexts. By integrating realistic trading tasks, crypto-specific risk metrics, and an open, reproducible benchmarking pipeline, our goal to promote more responsible and economically grounded research at the intersection of machine learning and quantitative finance.
Data Usage and Privacy. All datasets are publicly available cryptocurrency market data the asset level (e.g., prices and volumes). The dataset contains no personally identifiable information, private user data, or proprietary trading records. The benchmark operates exclusively on aggregate market statistics, ensuring compliance with standard data privacy and ethical research practices.
Bias, Market Impact, and Responsible Use. As with any financial system, models trained on his- torical market data may inherit structural patterns, regime dependencies, or latent biases present in past market behavior. While CTBench evaluates statistical fidelity and economic utility, it does not guarantee robustness under extreme market events or structural breaks. We encourage further search on robustness, stress testing, regime adaptation, and responsible deployment of Al systems in volatile financial environments. Additionally, synthetic data should be used cautiously, particularly in settings where model outputs could influence capital allocation or market dynamics.
Financial Use Disclaimer. This work is intended solely for academic research. The models, strate- gies, and results are presented to evaluate benchmarking methodology rather than to provide in- vestment guidance. Nothing herein constitutes financial advice, investment recommendation, dorsement of any asset, exchange, or trading strategy. CTBench should not be used for live trading or financial decision-making without independent validation and regulatory compliance.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the transparency and reproducibility of CTBench by providing open resources, detailed documentation, and standardized evaluation protocols.
Source Code. To facilitate replication and future extensions, we release the full implementation at: https://gi thub. com/Mi wewi 1et.l//eeee/ccciiieeeeee CTBench/ The repository contains all core components of CTBench, including data loaders. preprocessing pipelines, task definitions, evaluation metrics, and benchmarking scripts for TSG models. This enables end-to-end reproduction of both tasks and reported results.
Datasets. All cryptocurrency datasets used CTBench are publicly accessible. We provide detailed documentation of token selection criteria, time ranges, filtering rules, and preprocessing procedures Section 3.1 and Appendix C. These descriptions allow researchers to reconstruct the dataset directly from raw public sources.
Benchmark Protocol and Evaluation. CTBench evaluates eight representative TSG models under two complementary tasks: Predictive Utility and Statistical Arbitrage. Task formulations, model implementations, and experimental settings are described in Scction 4 and Appendices CDD All evaluation metrics, including error-based, rank-based, trading. risk, and efficiency measures, formally defined Section[..4aand Appendix C.4 Key hyperparameters for each model are reported in Appendix to ensure fair comparison and reproducibility.
Summary. Together, these resources provide a complete and extensible toolkit for reproducing our experiments and supporting future research time-series generation in financial domains.

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A NOTATIONS
Table summarizes the symbols and definitions used throughout the paper for quick reference.
B SCOPE ILLUSTRATION
To ensure clarity and fairness benchmarking, CTBench explicitly defines its scope across datasets, strategies, evaluation measures, and model families. This scoped design emphasizes crypto-specific characteristics, and provides a standardized basis for comparing diverse TSG methods. The followings detail these design choices.
avoids confounding factors,
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Table List of frequently used notations.
Symbol
Description
R Rext
Log-return matrix with assets and / hourly observations
r: = [ncd] E l
Log-retur vector of time t of all n assets
w,
Training window size, step, # of splits, split offset Training data of returns, test data of returns Time series generation (TSG) model
Rami: Riss
g
Rgen, Runin Rust
Generated time series, reconstruction of train and test sets Portfolio weight vector at hour t
Th = [nc.]] E R"
Vo. V1, and AV,
Initial capital, portfolio equity, and profit- and-loss hour Open, High, Low, and Close (OHLC) prices Multi-asset OHLC data array A feature set + with feature mapping function Φ
0, H., L,
D [0j,e]]
② (t))
Scope of Datasets. CTBench is restricted to cryptocurrency markets due to their unique properties, such 24/7 trading, high volatility, and fragmented liquidity. In selecting the concrete dataset, we focus on Binance USDT spot pairs (Binance Exchange (2025)) because Binance is consistently ranked as the largest centralized exchange by global spot volume and provides broad, liquid coverage of actively traded assets. Its freely accessible, high-quality historical data ensures that CTBench remains fully reproducible without requiring proprietary data contracts.
Moreover, we use only raw time series inputs (i.e., returns), excluding side-channel information (e.g., order books, blockchain logs, or news). This isolates core generative capabilities without reliance auxiliary signals. We employ only well-established financial features (e.g.. Alpha101 Kakushadzz 2016)) to ensure compatibility with real-world quantitative trading while minimizing noise from complex feature engineering.
Scope of Trading Strategies. capture diverse trading behaviors, we benchmark TSG models across three canonical strategies, ranging from rank-based to magnitude-sensitive and from direc- tional to market-neutral setups. This ensures a holistic evaluation of whether synthetic data general- izes across wolld trading paradigms merely overfits to specific signal patterns.
Scope of Evaluation Measures. Our benchmark incorporates a curated set of evaluation measures widely recognized in financial TSG research (Ang et al.) 2023a Wiese al., (020), ensuring holistic assessment of statistical fidelity and financial utility. We have excluded metrics with limited practical relevance or interpretability to maintain a focused and meaningful evaluation framework for the crypto domain.
Scope of TSG Models. We select TSG models capable of handling multivariate inputs typical in crypto markets, encompassing both general-purpose and finance-specine architectures. Our selec- tion spans five model families: GAN, VAE, diffusion, flow, and mixed-type, favoring architectures with general applicability over domain-speciffe requirements. All models are trained under a unified protocol without excessive hyperparameter tuning to ensure fair benchmarking and reflect practical deployment constraints.
C BENCHMARK DETAILS
C.I CRYPTO-CENTRIC DATASETS
Data Deseriptive Statisties. Understanding the statistical profile of crypto returns essential for designing effective TSG benchmarks. We analyze the distribution of log-returns identify devi- ations from normality, such as skewness and kurtosis, stylized facts well documented in financial time series. Cryptocurrencies in particular, often exhibit fat-tailed distributions, indicating ele- vated probability extreme price movements.
Figure [I presents histograms of the mean hourly log-return and mean hourly volatility (standard deviation of log-returns) across all 452 cryptocurrencies. The mean hourly returns are centered around zero but show slight right skew, suggesting modestly positive drift in most assets. In
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Number of Anoels
60
40
Number
800
120
20
10
-0.0006
-0.0004
0.0000 0,0022 0.0034
0.0038
0.0008
0.000
0.020
0.025
Mean Hourly Return
Hourty Volatilty
Figure 11: Histograms of the mean hourly log-return (%) (left) and mean hourly volatility (%) (right).
Figure 12: Line plots of closing returns for representative cryptocurrencies, with large-cap examples (top row), mid-cap examples (middle row), and small-cap examples (bottom row), displayed annu- ally from 2020 to 2024.
Nean Houry Retum (5)
Dantatto
0.000
0.00
22.01
-0.00
Hour Day (UTG)
Hour of Day (UTC)
Figure 13: The mean hourly log-return (%) (left) and mean hourly volatility (%) (right) by hour of day (UTC).
contrast, the mean hourly volatility exhibits a long right tail, indicating that while many assets trade with low volatility, a notable subset experiences highly volatile price swings.
To visualize market dynamics over time, categorize cryptocurrencies into large-, mid- and small- cap groups and plot representative closing prices annually from 2020 to 2024 in Figure These trajectories highlight significant market regimes, including the bull runs of 2020-2022 sharp rections in 2022, and subsequent periods of recovery or consolidation. Notably, mid- and small-cap assets often display greater volatility and sharper price swings than their large-cap counterparts.
Given that cryptocurrency markets operate 24/7, intraday patterns provide valuable insights into mar- ket microstructure. Figure [3 depicts the mean hourly log-return and volatility by time of day. We observe return peaks around early morning (5-7 A.M) and late evening (9-11 PM), reflecting height- ened trading during transitions between major global financial centers. Volatility peaks notably around midnight and during overlapping trading hours between US and Europe (12-5 PM), suggest- ing periods of intensified market activity driven by global participation and algorithmic strategies.
Discussions. Our analysis reveals several critical insights shaping the design of CTBench:
Complex Market Dynamics: Crypto markets exhibit high-frequency, high-dimensional behav- iors with distinct volatility profiles, intraday cycles, and regime shifts. These factors necessitate benchmarks tailored for crypto time series.
Benchmark Task Design: Given the data's complexity, evaluation tasks must probe whether synthetic data preserves predictive structures critical for practical applications such as forecasting and statistical arbitrage.
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TSG Model Requirements: Capturing the intricate temporal and cross-sectional dependencies of crypto markets demands advanced TSG architectures capable of modeling both short-term fluctuations and long-term trends.
Evaluation Metries: Assessing TSG performance in crypto markets requires multifaceted met- rics that go beyond statistical fidelity to capture financial viability and risk sensitivity.
Collectively, these insights underscore the need for crypto-specific benchmarks like CTBench to advance the evaluation and development of TSG models for this rapidly evolving domain.
C.2 DUAL-TASK EVALUATION
C.2.1 PREDICTIVE UTILITY TASK
Training Phase. Let (r) [r. [rr-wt1''' E Rax denote the real log-return matrix for
uain
split offset with length w 500 x 24 hours. TSG model g is trained on R(7) to capture both temporal dependencies and cross-sectional relationships. From this trained model, sample synthetic returns:
R = g(>), ~ NO, I)-
Next, features are extracted from Rgen via the pipeline: 4 (Rgge) E Raxaxd A forecasting model f : RRd + then predicts the next-hour return:
f = f( ( [t. t.:).
We use XGBoost (Chen & Guestrin 2016) as the forecasting model, chosen for its balance of robust- ness, interpretability, and minimal hy hyperparameter tuning (Vancsura et al.) 2025 Liu et al. 2021 Yun al. 202T), ensuring that benchmark results primarily reflect the quality of the generated data rather than model capacity.
Trading Phase. The trained forecaster is then applied to test period of length hours. For each hour and asset i, we predict fr,+1 = f Riea) (,:), rank the vector Fz41 [fr,+1] and construct dollar-neutral portfolio by longing the top half of assets (highest fr.--) and shorting the bottom half (lowest 11. 1. This portfolio is rebalanced hourly over the test window, maintaining balanced long and short exposures.
30 x 24
Discussions. This task reveals how well synthetic data generalizes to real markets, operationalizing the notion of functional realism. If Rgen preserves the predictive structures of R(T) the realized P&L AV; will score highly across CTBench's evaluation suite. Thus, synthetic data are valued not merely for statistical closeness to historical distributions but for the economic utility they unlock. Importantly, every component in Figure B(a) is modular: researchers can substitute alternative TSG models, forecasters (e.g., Transformers), feature sets, while retaining unified scoring framework.
C.2.2 STATISTICAL ARBITRAGE TASK
Training Phase. The Statistical Arbitrage task typically hinges on pairs or baskets of assets whose spreads revert toward long-term mean. In this task, residuals between real Rmain and reconstructed returns Riain are assumed follow mean-reverting dynamics. For asset and time we define training residual:
Pi.t Fi, fi..
where 7i,t Rarain and fs.e Rigain- For each asset these residuals are then fitted to Ornstein- Uhlenbeck (OU) process (Uhlenbeck & Ornstein 1930
dpi,t = 0.( Pt.)dt a.dWe
where 0; > 0 (mean reversion speed), μ4 (long-run mean), and (volatility) are estimated per asset, and dW; is a standard Wiener increment.
Trading Phase. On test data Riest the model reconstructs returns Rast. producing test residuals for Ts. Rees and f.A Russ
6 Fi.t fit--
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Each residual c, is converted to s-score:
E 14
81, 0:/ 02/
quantifying the deviation from equilibrium. Trading signals are then derived via:
Thresholding: Open or maintain a short position if Si. +7, a long if $1, < -y, otherwise stay flat, with y 2
Weight Normalization: Raw signals T/i sgn( (en |ssel are normalized to fk. =
na.// [ [).l))-
Execution: Portfolios are rebalanced hourly based on i
Discussions. The Statistical Arbitrage task evaluates whether reconstructed time series reveal stable, mean-reverting residuals suitable for statistical arbitrage, complementing the ceneration-focusee task by addressing market-neutral alpha extraction. These tasks ensure TSG models are tested not only for statistical fidelity but also for practical effectiveness in real-world crypto trading.
C.3 TRADING STRATEGIES
Beyond the datasets and dual-task evaluation, trading strategies play central role in assessing whether synthetic time scries capture signals that are economically meaningful. Since no single strategy fully characterize market behavior, CTBench incorporates multiple paradigms that flect how practitioners deploy forecasts in real trading:
S1: Cross-Sectionas Momentum (CSM) takes long positions in the top decile and short posi-
tions in the bottom decile of assets ranked by predicted one-hour returns. This probes a model's ability to capture ranking-boas based alpha signals.
S2: Long-Only Top- Top-Quantike (LOTQ) equally weights and goes long in the top 20% of assets based on predicted returns, with all other weights set zero. This isolates pure directional signals without short exposure.
S3: aypportional-eegghting (PW) allocates weights proportionally predicted returns: Miir /2, f),)), emphasizing the magnitude of forecasted signals rather than merely their ranks.
Each strategy exploits different statistical regularities, including level effects, cross-sectional disper- sion, and serial correlations, ensuring that no single modeling flaw remains undetected. They span the primary mandates seen on crypto desks: beta-neutral long-short equity, directional trend capture, and volatility harvesting. Finally, the CTBench pipeline is fully plug-and-play. Traders can drop any proprietary strategies without altering the benchmark code, fostering fair comparison across future studies.
C.4 FINANCIAL METRIC SUITE
Evaluating TSG models in crypto requires more than statistical similarity; it demands metrics that connect directly to financial relevance and practical usability. To this end, CTBench employs structured suite of measures that balance fidelity, predictive utility, trading performance, and risk management, while also accounting for computational efficiency.
Easor-based Evaluation. Given the actual return ri, and prediction Pr. for asset and time t:
E1: Mean Squared Error (MSE) is defined as:
1
MSE
(ri,tr Pr.4r))
t=1 1:
E2: Mean Absolute Error (MAE) defined as
MAE
Low values in both metrics reflect strong signal fidelity, while differences help distinguish outliers from widespread minor emror..
Rank-based Evaluation. Given realized returns r, and predictions én for all assets time t:
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E3: Information Coefficient (IC) is defined the average Spearman correlation between pre-
dicted and actual rankings, where IC,, SpearmanCor((:... P(tT). It is computed as:
IC = 8-8 TED t=1
10
E4: Information Ratio (IR) measures the stability of IC:
Mcan(ICr,.)
IR
Std(IC,,,)
A consistently positive IC shows the generator preserves rankings essential for long-short strategies, despite absolute errors.
Trading Performance. To assess economic utility, evaluate both profitability and risk-adjusted returns using the hourly profit-and-loss AV, and the simple return equity AV.VV-1 at time
E5: Compound Annual Growth Rate (CAGR) captures the annualized return based on equity growth, where Vo and V, are the initial and final equity, It is calculated as:
V
8760/s
CAGR
Vo
E6: Sharpe Ratio (SR) is defined as:
E BLAV//Vi- -8760
SR
Std Std(AV,/Vi-1)
These metrics capture both returns and the risk profile of symthetic-data-drivve strategies.
Risk Assessment Metries. Given profit-and-loss series AV, and simple return of equity AV,/V
E7: Maximum Drawdown (MDD) is defined as:
MDD max Vn V/ V
w
E8: Value Risk (VaR) 95% confidence is defined as:
VaR = Peercentile (AV.
E9: Expected Shortfall (ES) at 95% confidence defined as:
ESp.95 = - E[(AVi/VV-))) (AV./V.-1) < - VaRo.95
VaR captures potential worst-day losses, while ES reveals mean loss beyond that threshold, offering fuller picture of tail risk.
Efficiency. CTBench evaluates efficiency along two dimensions: training and inference time, to capture both scalability during model development and responsiveness in real-time use.
E10: Training Time is the wall-clock time at which a TSG model is trained.
E11: Inference Time is the mean wall-clock time to generate or reconstruct one batch of data (n assets 5 time steps).
C.5 TSG MODEL Zoo
GAN-based Methods. These methods (Seyfi et al. 2022 Wiese et al. 2020 Pei et al. 2021 Wang
ct al.
2023 leverage adversarial training to generate realistic series. They incorporate recurrent neural architectures and specialized attention mechanisms tailored to temporal dependencies.
M1: Quant-GAN (Wiese et al., 2020) approximates a trading utility function, optimizing the
generator for downstream profitability.
M2: COSCI-GAN (Seyfi et al.) 2022) integrates causal self-attention and statistical conditioning
to consider temporal order and cross-asset correlations.
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Table Summary of popular TSG methods with their backbone models and financial datasets used.
Year
Method
Backbone
Financial Datasets Used
2016
C-RNN-GAN (Mogren
2016
GAN
2017
RCGAN (Esteban et al.
2017
GAN
/
2018
T-CGAN Ramponi
2018
GAN
/
2019
TimeGAN
Yoon et al.
2019
GAN
Stocks
2019
WaveGAN
DDnahue ct al. 2019
GAN
/
2020
COT-GAN
Xu et al.
2020
GAN
/
2020
DoppelGAN ger (Lin et al.
2020
GAN
/
2020
Quant-GAN (Wiese ct al.
2020
GAN
SPX
2020
SigCWGAN (Ni
2020
GAN
SPX & DJI
2020
TSGAN (Smith Smith
2020
GAN
/ Stocks
2021
RTSGAN (Pei al. 2021
GAN
2021
Sig- Sig-WGAN (Ni al.
2021
GAN
SPX & DJI
2021
TimeGCI (Jarrett et al.
202
GAN
2022
CEGEN (Remlinger ct al.
2022
GAN
Stocks & Electric Price
2022
COSCI-GAN (Seyfi et al. 2022
GAN
2022
PSA-GAN (Jeha
2021
GAN
2022
TsT-GAN Srinivasan Knottenbelt
2022
GAN
Stocks
2022
TTS-GAN Li et al.
2022a
GAN
/
2023
AEC-GAN Wang al.
2023
GAN
/
2023
TT-AAE (Liu et al. 202.
GAN
Stocks
2021
TimeVAE Desai et al.
VAE
Stocks
2023
CRVAF (I.
2023
VAE
/
2023
TQNA (Lee et al.
2023
VAE
/
2024
KoVAE Naiman et al.
2024b)
VAE
Stocks
2023
Diff
(Coletta et al.
2023)
Diffusion
Stocks
2023
TSGM (Lmm al. 2023
Diffusion
Stocks
2024
Diffusion-TS Yuan & Qiao
2024
Diffusion
Stocks
2024
FIDE (Galib al.
20244
Diffusion
Stocks
2024
imagenTime (Naiman et al.
2024a
Diffusion
Stocks
2024
SDformer (Chen et al.
2024
Diffusion
Stocks
2025
PaD-TS Li et al. 2025
Diffusion
Stocks
2020
CTFP Deng al. 2020
Flow
2021
Fourier- Flow Alaa al.
2021
Flow
Stocks
2024
FlowTS Hu et al.
2024
Flow
Stocks
2018
Neural ODE Chen
2018
ODE RNN
/
2019
ODE-RNN Rubanova
2019
ODE RNN
/
2021 Neural SDE Kidger et al.
202
ODE GAN
Stocks
2022
GT-GAN Jeon et al. 2022
ODE GAN
Stocks
2023
LS4 (Zhou ct al. 20233
ODE + VAE
2024
TimeLDM (Qian et al. 2024)
Diffusion + VAE
Stocks
VAE-based Methods. These Methods use variational inference to capture both local and global
temporal patterns (Desai et al., 2021 Lee et 2023 Li
2023). They have shown strong
performance in general TSG tasks (Ang et al 2023al Bao et al. 2024).
M3: Time VAE DDsaa et al. 2021 is a sequence-awan VAE with temporal convolutions, de-
signed to capture both local and long-ran range dependencies in multivariate time series. M4: VAE (Naiman et al. 2024b) enhances Time VAE by incorporating Koopman operator- based latent dynamics for smoother and more interpretable generation.
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Diffusion-baset Methods. Diffusion models Yuan & Qiao 2024; Galib et al. 2024 Chen al., 2024 Li et al. 2025; Naiman et al. 2024a) progressively convert noise into structured data via iterative denoising, proving highly effective in modeling complex market dynamics.
M5: Diffusion-TS (Yuan & Qiao, 2024) is a score-based diffusion model that refines Gaussian noise into realistic trajectories, achieving state-of-the-an sample fidelity financial data. M6: FIDE (Galib et al., 2024) introduces factorized conditional diffusion with attention-driven score networks, enabling conditional generation based on market regimes liquidity factors.
Flow-hased Methods. These methods (Alaa et al. 202 Hu et al. 2024) employ invertible trans- formations model data distributions, ensuring exact likelihood estimation and efficient sampling.
M7: Fourier-FFee Flow (Alaa et al., 2021) uses frequency-domain coupling layers for invertible transformations, allowing fast sampling and exact likelihood computation while preserving pe- riodic structures.
Mixed-based Methods. Hybrid models (Zhou 2023
Rubanova et al. 2019 Jeon al. 2022)
typically combine multiple modeling paradigms (e.g., ODEs and VAEs) to capture nuanced temporal dynamics and stochastic characteristics.
M8: LS4 [Zhou et al., 2023) fuses deep state-space modeling with stochastic latent variables via variational inference, providing flexible and interpretable modeling of complex crypto dynamics.
D EXPERIMENTAL SETUP
Datasets. We employ the datasets (Binance Exchange,20222 2025b) described in $3.1 for the experiments. To simulate real-world deployment, we adopt walk-forward rolling-window validation scheme, using 500 days of hourly data for training, and 30 or 15 days for testing on the Predictive Utility and Statistical Arbitrage tasks, respectively. After each cycle, the window advances by the test period length, with models retrained. This process spans from January 2020 to December 2024, covering diverse market regimes.
Benchmark Configurations. To isolate core TSG model performance, we assume zero trading fees by default in both Predictive Utility and Statistical Arbitrage tasks, enabling fair comparison of signal quality without interference from platform-specific costs. For the Statistical Arbitrage task, we also apply a 0.03% trading fee, reflecting the fee level that typical liquidity provider achieve major centralized exchanges (Zhang et al.) 2023 Winkel & Härdle 2023 Binance Exchange| 2025a). providing a more grounded evaluation net profitability.
Trading Strategies. For the Predictive Utility task, employ three representative trading strate- gies in $3.3 to evaluate synthetic data across varied portfolio constructions. In contrast, the Statis- tical Arbitrage task employs the mean-reversion strategy to isolate the model's ability preserve exploitable residual structures.
We evaluate eight representative TSG models across five major families in $3.5 Hyperparameter settings follow published recommendations or are tuned for stable training.
TSG Methods.
GAN-based: Quant-GAN adopts fntent_dim 8, fadden_ding 80, gradient penalty >gp 10.0,
and critic steps Reeritic 5; COSCI-GAN uses latent dim = 32, 7 = 5, and Rggroups = 4 with
MLP-based central discriminators, as per (Seyli et al. 2022
VAE-based: VAE uses latent dim = 8 with stacked hidden layers of 50, 100, and 200 units; KoVAE follows (Naiman al., 2024b), setting WKL 0.009 and WPRED 0.03 for KL and
auxiliary loss terms.
Diffusion-based: Diffusion-TS uses 1000 timesteps, 3 encoder layers, decoder layers, and dmodel 64; FIDE applies 1000 steps, hidden_dim = 64, layers, and = 0.05.
Flow-based: Fourier-Flow incorporates DFT-based coupling layers with hidden,size = 128 and flow layers.
Mixed-type: LS4 employs hidden dim = 6, latent dim = 8, and a batch size 512.
Financial Baseline Models. We added two baselines commonly used in quantitative finance:
ARMA-GARCH (Engle 1982): We multivariate VAR-DCC-GARCH models, which are
multivariate extensions of univariate ARMA-GARCH models, to jointly capture the conditional
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Cuars
COSCT-4
KOVE
RIB
Fourerfice
154
ARUW-GARCH
Bod8773
CBM
10"
LOTG
homal cec
10
10
19'1 00107200 mnror enoee stres creee oroote conoes
107
2007 orotee 51 07200977009 01P50 orate serorree
10
1811 arace crpse saoun sreen stavin
Figure 14: Simulated growth curves of $10,000 over four years under three trading strategies.
mean, volatility, and dynamic correlation structure of returns. Specifically, we use VAR(1) spec- ification for the conditional mean (corresponding to ARMA(1,0) structure in each marginal series), univariate GARCH(1,11 processes for the conditional variances, and a DCC(1,1) structure for the time-varying conditional correlations.
Bootstrap (Rubin, 1981): implement bootstrap-style generator that resamples returns to preserve marginal distributions and local dependence structures without explicit parametric dy- namics.
These models evaluated under the same Predictive Utility task the learning-basse TSG models. In addition, for the Statistical Arbitrage task, we use the ARMA-GARCH model's filtered (fitted) conditional mean process reconstructions in 43.2
Evaluation Metrics. We adopt the thirteen metrics detailed $3.4 thereby scoring each model on forecasting accuracy, rank correlation, trading profitability, tail risk, and computational efficiency.
Experiments Environments. All experiments are conducted on a machine equipped with an Intel Xeon R Platinum 8480C @ @3.80GHz, 64 GB RAM, and NVIDIA H100 GPU.
E ADDITIONAL RESULTS ON EQUITY CURVE DYNAMICS
E.1 PREDICTIVE UTILITY TASK
Figure 14 presennts log-scaled equity curves (initialized at $10,000) for each TSG model under three trading strategies from 2021 to 2024, highlighting cumulative returns and how inductive biases in- teract with shifting market regimes.
Under CSM, COSCI-GAN and VAE deliver steady gains by effectively preserving rank order and alpha signals, though both limit upside potential by dampening extreme winners. In contrast, Diffusion-TS and FIDE exhibit persistent declines, as their denoising processes suppress volatility and weaken long-short execution.
Under LOTQ, COSCI-GAN clearly outperforms, likely benefiting from adversarially enhanced right-tail signals that capture strong directional momentum. Time VAE and Fourier-Flow sustain modest but stable growth, whereas Diffusion-TT falters by missing rare yet critical upward spikes.
Finally, under PW, which emphasizes consistent pairwise rankings, COSCI-GAN again dominates. TimeVAB and Fourier-Flow show smooth compounding, reflecting robust generalization from well- regularized latent spaces. By contrast, LS4 remains largely flat across all strategies, suggesting its conservative design behaves more like a low-beta portfolio than an alpha-generating model.
E.2 STATISTICAL ARBITRAGE TASK
Figure 15 illustrttss the equity curves under the Statistical Arbitrage task, starting from $10,000 and incorporating 0.03% trading fees.
At the top end, LS4 compounds almost monotonically, underscoring its strong fee resilience, with two staircase-like surges in mid-2022 and early 2023. This pattern suggests that its latent-switching mechanism is particularly effective at capturing regime shifts rather than merely reacting to incre- mental meva-seversioo. Ko follows with similarly convex trajectory. initially smooth with shallow drawdowns through 2023, though its growth fades amid the turbulence of 2024. Time maintains steady gains through 2022, plateaus in mid-2023, and drifts sideways slightly down-
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Published as a conference paper at ICLR 2026
Diffusion-TS
ARMA-GARCH
Normalized Equity
10
10
2021-07
2022-01
2022-07
2023-01
2024-01
2024-07
2025-01
Figure 15: Simulated growth curves of $10,000 for the Statistical Arbitrage task (with 0.03% fee).
ward in 2024, reflecting its dependence on residual signals that weaken as secsinecctionl dispersion compresses.
Among mid-tier performers, Diffusion-TT TS delivers a notably stable curve with minimal drawdowns, though its terminal return is the lowest among viable models, consistent with its characterization as a fee-resilient, lisk-balanced generator.
At the lower end, FIDE collapses early, suggesting that its residuals may be over-regularized to the point eliminating tradable structure. Meanwhile, Fourier-Flow experiences slow but persistent bleed after mid-2022, likely driven by over-smoothed residual patterns that generate sustained negative carry.
F THE USE OF LARGE LANGUAGE MODELS (LLMS)
LLMs are used mainly as auxiliary tools to aid and polish the writing of the paper.
24


Reviews:
[{"review_id": "lAXiLvNs7p", "reviewer": "Reviewer_hL6x", "summary": "The paper introduces CTBench, an open-source benchmark for crypto time‑series generation. It provides a curated, hourly panel of 452 USDT pairs on Binance (2020–2024) and a two-track evaluation for crypto time-series generation. Representative generators are tested across four market regimes using a finance-first metric stack that spans several aspects. The results document model utility in different regimes.", "questions": "- Do you evaluate the covariance and eigenspectrum alignment between synthetic and real returns? This might explain why Fourier-Flow yields stable rank metrics but limited arbitrage.\n\n- Compared with those used in finance economics, such as ARMA-GARCH and the bootstrap, do the learning-based TSG models provide better synthetic data?\n\n- Have you tested alternative forecasters (e.g., linear model, random forest, and MLP)? Do the relative model performance and rankings persist?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Empirically, the authors observe the regime-dependent trade-offs between fidelity and tractability, i.e., high fidelity does not directly imply tradability. This is an important insight for generating synthetic financial data.\n\n- Emphasizing rank-based metrics and arbitrage capacity over purely generative scores moves the discussion toward decision-useful validation.\n\n- The authors offer useful guidance on model selection for practical uses.", "weaknesses": "- Results are tied to a single forecasting model. Without testing a variety of forecasters, it’s unclear whether conclusions generalize.\n\n- Insufficient diagnostic analysis of model performance differentials. The paper does not investigate why the TSG models diverge in outcomes, leaving the observations unexplained.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "31u9clljEF", "reviewer": "Reviewer_8b4R", "summary": "This paper provides an open-source cryptocurrency dataset and the first benchmark for time series generation in cryptocurrency, named CTBench. Moreover, it introduces a novel dual-task evaluation framework to assess how well the temporal dynamics captured by synthetic data can be used for downstream forecasting and whether they can reconstruct market structure and isolate tradable signals. CTBench specifically evaluates the capability of eight state-of-the-art generative models for time series, using abundant metrics in different financial applications. Experimental results benchmark the performance of synthetic data from tested generative models.", "questions": "- Is it possible that generative models may have different performance using forecasting models other than XGBoost?\n    \n- Are all feature windows strictly backward-looking and computed within the split? Are any scalers fit only on training data? Do all the results hold using different splits?\n\n- A small typo: Citation formatting at line 041 is incorrect.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper offers a valuable open-source cryptocurrency dataset, which bridges the gap between research on time series generation and cryptocurrency.\n    \n- The benchmark discussed in this paper is comprehensive, involving most of recent deep generative models and evaluation metrics widely used in finance.\n    \n- This paper provides some intriguing practical findings that are critical for future research and applications: (i) current generative models achieve diverse performance across tasks, none of them can outperform others across all tasks and metrics; (ii) in statistical arbitrage task, different generative models are sensitive to distinct scenarios; (iii) noise in financial data is important as well.\n    \n- Beyond the above performance, this paper further evaluates the efficiency of generative models.\n    \n- The writing of this paper is well-structured and easy to read.", "weaknesses": "- The predictive utility task fixes the forecasting model to XGBoost for its robustness and interpretability. However, some conclusions may change with alternative predictors, given that different setups have large impacts on the model performance.\n    \n- [Minor] Dual-task evaluation module in Figure 2 is visually unclear. Perhaps its readability can be improved by simplifying the contents like the other modules, since Figure 3 already shows the details.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kJXxFI8kS8", "reviewer": "Reviewer_WGHC", "summary": "CTBench is the benchmark tailored for Time Series Generation  in cryptocurrency markets, addressing limitations of existing benchmarks in domain generality, task scope, and crypto-specific evaluation. It features a curated dataset of OHLC data, dual-task evaluation, 13 financial metrics across 5 dimensions, and 8 TSG models from 5 families. Experiments across bull, volatile, consolidation, and mean-reverting regimes reveal trade-offs between statistical fidelity and trading profitability. CTBench enables rigorous TSG evaluation for crypto trading, bridging synthetic data generation with practical financial utility.", "questions": "See Weakness.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Captures unique traits of crypto markets including 24/7 volatility, absence of intrinsic valuation and irregular liquidity that are not addressed in traditional benchmarks.\n2. Links TSG to real-world use cases through two complementary tasks focusing on forecasting capabilities and tradable signal extraction rather than just statistical similarity.\n3. Integrates 13 diverse metrics covering error measurement, rank correlation, trading performance, risk assessment and computational efficiency for comprehensive evaluation of synthetic data’s financial utility.\n4. Benchmarks 8 representative TSG models spanning five architectural families across four distinct market regimes, delivering actionable scenario-specific recommendations for practitioners.", "weaknesses": "1. Relies solely on Binance’s spot hourly data, lacking the diversity of cross-exchange data, alternative crypto asset types such as futures contracts, and different sampling frequencies. Besides, it remains unknown whether this generation method can be effectively extended to a broader range of asset classes, which limits the method’s scalability.\n2. All TSG models' generated data are used with the same XGBoost model for downstream prediction. Different TSG models may be better suited for different types of predictors as some models like Transformer are more sensitive to long-term dependencies, so using a fixed predictor may introduce evaluation bias and fail to fully reflect the generality of the generated data.\n3. Diffusion-based models included in the benchmark may exhibit long training and inference times, providing comparisons of generation efficiency across different models would help alleviate concerns in this regard.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)