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TimeGPT-1
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TimeGPT-1
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In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training. We evaluate our pre-trained model against established statistical, machine learning, and deep learning methods, demonstrating that TimeGPT zero-shot inference excels in performance, efficiency, and simplicity. Our study provides compelling evidence that insights from other domains of artificial intelligence can be effectively applied to time series analysis. We conclude that large-scale time series models offer an exciting opportunity to democratize access to precise predictions and reduce uncertainty by leveraging the capabilities of contemporary advancements in deep learning.
Forward citations
Cited by 38 Pith papers
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TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
TSFMAudit detects pretraining contamination in time series foundation models via probe adaptation dynamics (faster loss drop, smaller backbone shift), tested on 6 models and 187 datasets against 10 LLM-derived baselines.
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MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios
MacroLens is a point-in-time multi-signal benchmark dataset and seven tasks for evaluating contextual financial reasoning models under macroeconomic scenarios.
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GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
GNSS-FM is a self-supervised foundation model for GNSS displacement time series that outperforms task-specific baselines on 90-day forecasting and seismic step localization after pretraining on global station data.
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Discrete Prototypical Memories for Federated Time Series Foundation Models
FeDPM learns and aligns local discrete prototypical memories across domains to create a unified discrete latent space for LLM-based time series foundation models in a federated setting.
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TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
TS-Reasoner is a domain-oriented agent using LLMs, computational tools, and error feedback for multi-step time series inference, showing better performance than general LLMs on understanding and reasoning benchmarks.
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Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
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A decoder-only foundation model for time-series forecasting
A pretrained decoder-only patched transformer achieves near state-of-the-art zero-shot forecasting performance across diverse time series datasets and settings.
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Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting
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%.
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A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
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.
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Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks
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.
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Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
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Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule
Zero-shot Chronos wins time-series benchmarks by under-extrapolating trend, and trend strength computed before forecasting predicts when it will beat classical models.
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Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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Frequency-Guided Deformable Networks for Continuous Phase Alignment
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.
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TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
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.
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Tube Loss: A Novel Approach for Prediction Interval Estimation
Tube Loss is a novel loss function enabling simultaneous prediction interval bound estimation with asymptotic coverage guarantees, tunable positioning for skewed distributions, and trade-offs between coverage and widt...
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RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
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.
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Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
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.
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Modular Foundation Models for Time-Series Perception in Digital Twins
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.
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Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
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Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings
Empirical tests show TSFMs like Chronos-2 and MOMENT need fine-tuning for E-Nose data, with fusion to specialized models further improving gas identification and concentration prediction.
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Pretrained Time-Series Foundation Models for Financial Return Forecasting
Pretrained TSFMs achieve top ranks on equity return tasks but show sparse, minimal improvements over random walk, serving as practical priors without reliable alpha generation.
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WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
WEQA proposes a query-adaptive agent framework combining LLMs with wearable data tools, achieving 24% higher accuracy than baselines on a benchmark from four open datasets, with gains in expert-rated usefulness.
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DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids
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.
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UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
UPLOTS proposes a unified prompt-guided pretrained transformer for generating constrained time-series data across diverse domains using dynamic multi-dataset loss re-weighting.
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ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
ChronosAD is a two-stage anomaly detection architecture that applies a time series foundation model for zero-shot embeddings followed by a custom Temporal Block, reporting average gains of 4.72% AUC and 6.60% AP over ...
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FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting
FHRFormer introduces a self-supervised masked transformer framework for inpainting and forecasting fetal heart rate time-series to handle signal dropouts in continuous monitoring.
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Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
Unicorn uses a latent prototype codebook to learn identity-agnostic correlation patterns for scalable pretraining and improved few-shot forecasting on high-dimensional time series across heterogeneous datasets.
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Wearable AI in the Era of Large Sensor Models
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
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Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with...
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Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
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Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail
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.
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Post-Training in Time Series Foundation Models: A Unifying Framework
A survey that groups time series foundation model post-training methods into five families based on where they intervene in the prediction pipeline.
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From Vector Autoregressions to AI-based Time Series Forecasting: A Review
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.
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Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models
Fine-tuning the MOMENT foundation model on simulated RPL statistics enables multi-class attack identification with performance comparable to state-of-the-art methods.
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Deep Learning Network-Temporal Models For Traffic Prediction
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.
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Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Fine-tuning TimesFM sequentially on new synthetic time-series data causes measurable forgetting of earlier tasks, with higher learning rates producing stronger forgetting.
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