TimeGPT-1

1. Byte Pair Encoding for Efficient Time Series Forecasting

   cs.LG 2025-05 conditional novelty 7.0 of 10

   A byte-pair-encoding tokenizer that converts repeated temporal motifs into single tokens improves zero-shot forecasting accuracy and speed over sample-wise and patch-based methods.

2. Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting

   cs.LG 2026-07 conditional novelty 6.0 of 10

   A residual-guided, coarse-to-fine inference wrapper consistently improves frozen time-series foundation models for monthly drought-index forecasting, cutting one-month-ahead MSE by up to 18.9%.

3. A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

   cs.LG 2026-07 conditional novelty 6.0 of 10

   Transformers—especially a standard encoder-decoder—yield the lowest hourly load forecast errors across TSO, low-voltage feeder, and client-level datasets, with 6.6–10.7% error reduction over the best non-Transformer baseline.

4. Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks

   q-fin.ST 2026-07 accept novelty 6.0 of 10

   Zero-shot time series foundation models largely fail to beat econometric benchmarks for realized volatility forecasting, with only TTM achieving a narrow, calibration-driven edge.

5. Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule

   stat.AP 2026-07 conditional novelty 6.0 of 10

   Zero-shot Chronos wins time-series benchmarks by under-extrapolating trend, and trend strength computed before forecasting predicts when it will beat classical models.

6. Frequency-Guided Deformable Networks for Continuous Phase Alignment

   eess.SP 2026-03 conditional novelty 6.0 of 10

   RFFT-derived periods guide deformable convolutions with Gaussian RBF interpolation and asymmetric routing to improve multi-task time-series modeling over rigid grids and bilinear sampling.

7. TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models

   cs.LG 2026-01 conditional novelty 6.0 of 10

   TimeSAE trains a sparse autoencoder with counterfactual and consistency losses to explain black-box time series predictions, claiming better faithfulness and out-of-distribution robustness than eight baselines.

8. CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams

   cs.LG 2025-08 reject novelty 6.0 of 10

   CALM uses an LLM-as-a-Judge to curate anomalies for continuous fine-tuning of a time-series foundation model, improving anomaly detection on held-out stream segments.

9. Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models

   cs.LG 2025-07 conditional novelty 6.0 of 10

   Pre-trained multivariate time-series imputation models frequently return values that violate known relations between variables, and a diffusion-based score can detect and filter these errors.

10. Time Series Foundation Models for Multivariate Financial Time Series Forecasting

    q-fin.GN 2025-07 reject novelty 6.0 of 10

    Pretrained TTM shows large transfer and sample-efficiency gains in three financial forecasting tasks relative to training from scratch, but methodological flaws including possible look-ahead bias weaken the quantitati...

11. Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A foundation model of wearable behavioral data outperforms simple baselines and complements a PPG sensor model across 57 health detection tasks.

12. Towards Time Series Generation Conditioned on Unstructured Natural Language

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A diffusion model with BERT language conditioning can generate simple 100-step time series from natural language prompts, supported by a new 63,010-pair dataset.

13. BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A balanced sampling strategy over statistically characterized time series patterns lets universal forecasting models train on 78 billion tokens instead of 419 billion, with equal or better zero-shot accuracy.

14. Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions

    cs.LG 2025-05 reject novelty 6.0 of 10

    CHARM is a 7M-parameter self-supervised embedding model for multivariate time series that uses channel descriptions to beat specialized baselines on forecasting, classification, and anomaly detection.

15. Does Scaling Law Apply in Time Series Forecasting?

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A parameter-light adaptive linear model (ALinear) outperforms larger baselines on long-horizon univariate forecasting benchmarks while using under 1% of their parameters, but the efficiency comparison rests on questio...

16. Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics

    cs.LG 2025-04 conditional novelty 6.0 of 10

    Fine-tuning MOMENT time series foundation models reduces reconstruction loss but does not visually improve the interpretability of their latent space projections in the DeepVATS visual analytics environment.

17. One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A masked conditional diffusion model with historical user embeddings simultaneously performs trajectory generation, recovery, and prediction, beating task-specific baselines on two datasets.

18. Multi-source Multi-level Multi-token Ethereum Dataset and Benchmark Platform

    cs.CE 2025-01 conditional novelty 6.0 of 10

    3MEthTaskforce is a new multi-source Ethereum dataset combining transactions, token prices, market indices, and Reddit sentiment, with benchmarks for user behavior and price prediction.

19. Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

    q-bio.NC 2025-01 conditional novelty 6.0 of 10

    A systematic review of 119 ANN-MEG studies shows rapid growth across decoding, BCI, clinical, modeling, and source-localization applications, with recurring reproducibility gaps.

20. Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

    cs.LG 2025-01 conditional novelty 6.0 of 10

    TAFAS improves frozen time series forecasters at test time by calibrating inputs and outputs with periodicity-scheduled partial ground truth.

21. Time Series Language Model for Descriptive Caption Generation

    cs.CL 2025-01 conditional novelty 6.0 of 10

    TSLM combines a tagged textual view and a reprogrammed embedding view of a time series with LLM-generated, scorer-filtered training data to produce state-of-the-art time series captions on the STOCK and SYNTH benchmarks.

22. TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Retrieving similar time-series segments from a multi-domain knowledge base and injecting them through a learned Channel Prompting module improves zero-shot forecasting of a frozen TSFM, though gains are small and leak...

23. ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data

    cs.CL 2024-12 conditional novelty 6.0 of 10

    ChatTime tokenizes time series as discrete foreign-language words, continually pre-trains and instruction-tunes LLaMA-2-7B via LoRA, and shows competitive zero-shot forecasting plus bimodal text and time series tasks.

24. Federated Foundation Models on Heterogeneous Time Series

    cs.LG 2024-12 conditional novelty 6.0 of 10

    FFTS is a federated pretraining framework with a timescale-aware mixture-of-experts module that trains a time series foundation model from scratch across heterogeneous, non-shared datasets.

25. On Foundation Models for Dynamical Systems from Purely Synthetic Data

    cs.LG 2024-11 conditional novelty 6.0 of 10

    Pretraining a transformer purely on synthetic dynamics sampled from a reproducing kernel Hilbert space yields zero-shot and fine-tuned state predictors that beat non-pretrained baselines on cart-pole and Furuta systems.

26. FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting

    cs.LG 2026-08 conditional novelty 5.0 of 10

    Federated LoRA fine-tuning of a pre-trained time-series model on Indian market price data overfits, but differential privacy noise at epsilon=5 acts as regularization and cuts mean absolute percentage error by 31% rel...

27. RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment

    cs.LG 2026-07 conditional novelty 5.0 of 10

    RAG-HAR+ adapts retrieval features offline via an LLM agent and routes only ambiguous windows to the LLM, cutting online LLM cost by ~90–99.9% with competitive accuracy on six HAR benchmarks.

28. Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Inference-time wrappers that add multi-resolution residual corrections or block-bootstrap averaging to frozen time-series foundation models reduce MSE for regional one-month-ahead SPEI forecasts.

29. Modular Foundation Models for Time-Series Perception in Digital Twins

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A gated bank of frozen self-supervised time-series encoders, aligned and aggregated by a Transformer, supports competitive multi-task perception for digital twins and hydro-generator virtual sensing.

30. DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids

    math.OC 2026-06 reject novelty 5.0 of 10

    DRP-FLR clusters historical daily load curves to estimate each entity's demand-response potential and selects participants via MILP, claiming 36.63%–91.87% lower regulation deviation.

31. Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

    cs.LG 2025-06 conditional novelty 5.0 of 10

    No time series foundation model statistically outperforms the biseasonal MSTL model in most European day-ahead electricity price markets in 2024, though Chronos-Bolt and Time-MoE match traditional methods.

32. When can isotropy help adapt LLMs' next word prediction to numerical domains?

    cs.CL 2025-05 reject novelty 5.0 of 10

    Using a log-linear model and Jacobian analysis, the paper claims isotropy in LLM hidden embeddings stabilizes the softmax partition function and improves time-series forecasting, with illustrative experiments on five ...

33. Zero-Shot Forecasting Mortality Rates: A Global Study

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Zero-shot CHRONOS forecasts are competitive for 5 and 10 year mortality horizons, TimesFM underperforms, and a mortality-trained random forest achieves the lowest SMAPE overall.

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

    q-fin.ST 2025-05 conditional novelty 5.0 of 10

    The paper reports that a time-series transformer pretrained on public and proprietary financial data becomes competitive on financial tasks after fine-tuning, while zero-shot general forecasting remains behind MOIRAI.

35. How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Chronos, a pretrained foundation model used zero-shot, beat 16 other models at forecasting Everglades water levels across five stations and horizons from 7 to 28 days.

36. Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Dual-Forecaster demonstrates that jointly using historical descriptive text and future predictive text improves time series forecasting accuracy over text-free and single-text baselines.

37. A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions

    cs.LG 2025-02 reject novelty 5.0 of 10

    A hybrid quantile-averaging method that pins a probabilistic forecast to an expert point forecast is reported to improve contraceptive demand forecasts, but the headline results are internally inconsistent.

38. A Survey on Time-Series Distance Measures

    cs.DB 2024-12 conditional novelty 5.0 of 10

    A survey classifying over 100 time-series distance measures into seven families and proposing a dependent/independent framework for multivariate extensions.

39. DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework

    cs.LG 2024-12 reject novelty 5.0 of 10

    DDD-GenDT uses an LLM ensemble with sliding observation windows to zero-shot predict CNC spindle current, reaching 0.48 A RMSE on the NASA milling dataset.

40. Time Series Foundational Models: Their Role in Anomaly Detection and Prediction

    cs.LG 2024-12 conditional novelty 5.0 of 10

    Across five public datasets, weighted XGBoost and autoencoder baselines match or beat time series foundation models on anomaly detection and prediction, at a fraction of the computational cost.

41. Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail

    cs.LG 2026-07 conditional novelty 4.0 of 10

    A smooth-baseline-plus-sparse-shock decomposition lowers forecast variance and safety stock but increases stockout costs, reducing total inventory cost only when holding costs exceed ~20% of stockout costs.

42. Post-Training in Time Series Foundation Models: A Unifying Framework

    cs.LG 2026-07 accept novelty 4.0 of 10

    A survey that groups time series foundation model post-training methods into five families based on where they intervene in the prediction pipeline.

43. From Vector Autoregressions to AI-based Time Series Forecasting: A Review

    econ.EM 2026-07 unverdicted novelty 4.0 of 10

    AI forecasting methods are flexible generalizations of the classical VAR's conditional forecast distribution, gaining adaptability and scale but losing ready-made inference, identification, and structural interpretation.

44. Deep Learning Network-Temporal Models For Traffic Prediction

    cs.LG 2026-03 conditional novelty 4.0 of 10

    Cluster-CALF (Spearman-clustered cross-modal LLM fine-tuning) lowers mean sMAPE by about 41% versus a tuned LSTM and further reduces error variance on hourly backbone traffic MTS.

45. Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting

    cs.LG 2025-10 reject novelty 4.0 of 10

    Fine-tuning TimesFM sequentially on new synthetic time-series data causes measurable forgetting of earlier tasks, with higher learning rates producing stronger forgetting.

46. On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating

    cs.LG 2025-08 conditional novelty 4.0 of 10

    On four public datasets, Gradient Boosting with hand-built features beat Chronos, Llama, and ARIMA on most accuracy metrics, while Chronos only led on financial sMAPE.

47. Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles

    cs.LG 2025-08 conditional novelty 4.0 of 10

    On one Belgian electricity load series, bagging, regression stacking, and residual correction reduce MSE relative to standalone Lag-Llama and AutoGluon forecasts, though the reported numbers are inconsistent and lack ...

48. Foundation Models for Demand Forecasting via Dual-Strategy Ensembling

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A dual ensemble of hierarchical partitions and diverse backbones improves foundation-model sales forecasts on M5 and three external datasets, though the zero-shot protocol is under-specified.

49. Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A univariate VAE with dilated convolutions is proposed as a simple 'foundation' model for time-series anomaly detection, with preliminary zero-shot experiments on two datasets.

50. SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs

    cs.CL 2025-06 conditional novelty 4.0 of 10

    SEED is an LLM-based forecasting architecture that couples variable-wise attention encoding with prototype-based semantic reprogramming and a frozen decoder.

51. Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Delayformer embeds each time series variable into a Hankel matrix, processes the matrices as images with a shared ViT, and predicts all variables in parallel.

52. Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed

    cs.LG 2025-05 conditional novelty 4.0 of 10

    Tube loss based LSTM, GRU, and TCN models produce narrower, well-calibrated wind speed prediction intervals than six baseline methods on three datasets.

53. Foundation Models for CPS-IoT: Opportunities and Challenges

    cs.LG 2025-01 conditional novelty 4.0 of 10

    Current foundation models fall short on CPS-IoT needs in resource efficiency, spatial generalization, long-term context, and knowledge integration; the paper proposes desiderata and a community roadmap.

54. MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models

    cs.NI 2025-01 reject novelty 4.0 of 10

    MoE2 trains a gating network once on all edge LLMs, then selects a resource-feasible subset and a per-prompt top-k of experts to fuse token probabilities under latency and energy budgets.

55. Comparative Analysis of Zero-Shot Capability of Time-Series Foundation Models in Short-Term Load Prediction

    eess.SY 2024-12 conditional novelty 4.0 of 10

    Zero-shot time-series foundation models, especially Chronos, outperform trained GP and SVR baselines on short-term load prediction across 11 UK, German, and Dutch datasets.

56. Financial Fine-tuning a Large Time Series Model

    q-fin.CP 2024-12 conditional novelty 4.0 of 10

    Fine-tuning TimesFM on log-transformed financial price data improves directional accuracy and mock-trading Sharpe ratios over the base model.

57. Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting

    cs.LG 2024-12 conditional novelty 4.0 of 10

    A simple gradient-free 'directional gradient approximation' attack makes LLM time series forecasters degrade more than equivalent random noise, across GPT-3.5, GPT-4, LLaMa, Mistral, TimeGPT, and TimeLLM.

58. Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection

    physics.geo-ph 2024-12 reject novelty 4.0 of 10

    Fine-tuned TimeGPT forecasts some well logs reasonably, but the claimed 93% anomaly detection accuracy is contradicted by ROC-AUC near 0.51 and MCC near zero.

59. Causal Time-Series Synchronization for Multi-Dimensional Forecasting

    cs.LG 2024-11 conditional novelty 4.0 of 10

    Aligning cause-effect pairs by their estimated Granger lag improves channel-dependent forecasting accuracy and transfer learning on synthetic time-series data.

60. A Survey of AIOps in the Era of Large Language Models

    cs.SE 2025-06 conditional novelty 3.0 of 10

    A systematic survey that categorizes LLM-based AIOps research into four dimensions: data sources, tasks, methods, and evaluation, claiming to be the first comprehensive such overview.

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