TimeGPT-1
Reviewed by Pithpith:JV7BRMFTopen to challenge →
read the original abstract
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.
This paper has not been read by Pith yet.
Forward citations
Cited by 24 Pith papers
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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...
-
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.
-
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.
-
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.
-
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.
-
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.
-
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 ...
-
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.
-
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.
-
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.
-
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...
-
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.
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.