Time Series Foundation Models
PulseAugur coverage of Time Series Foundation Models — every cluster mentioning Time Series Foundation Models across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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HoliBench toolkit enables cross-platform evaluation of foundation models
A new toolkit called HoliBench has been developed to address the challenges of deploying foundation models, including large language models, on resource-constrained CPS-IoT applications. This toolkit offers a unified wo…
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New method speeds up training for Time Series Foundation Models
Researchers have introduced Synthetic Data Distillation (SDD), a novel training objective for Time Series Foundation Models (TSFMs). SDD enhances pre-training by comparing TSFM outputs to the conditional forecast distri…
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AI in Finance: Progress Limited for Consistent Profitability
A new research paper reviews the current state of artificial intelligence in equity and crypto markets, examining its progress from data analysis to automated investing. While AI has shown advancements in prediction, te…
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Research reveals universal redundancies in Time Series Foundation Models
A new research paper published on arXiv details findings about universal redundancies in Time Series Foundation Models (TSFMs). The study, conducted by Anthony Bao and others, utilized mechanistic interpretability tools…
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Decathlon adopts Time Series Foundation Models for scalable demand forecasting
Decathlon is transitioning its demand forecasting operations to Time Series Foundation Models (TSFMs) to address the scalability and efficiency challenges of managing forecasts for tens of thousands of products across m…
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New 'Living Benchmark' LiveHouse-TS Challenges Static Time Series Model Evaluation
Researchers have introduced LiveHouse-TS, a novel benchmark infrastructure designed to evaluate Time Series Foundation Models (TSFMs) in dynamic, real-world conditions. Unlike traditional static benchmarks, LiveHouse-TS…
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New OATS strategy enhances Time Series Foundation Models with dynamic data augmentation
Researchers have developed OATS, a novel online data augmentation strategy for Time Series Foundation Models (TSFMs). This method dynamically generates synthetic data tailored to specific training stages, using valuable…
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New methods accelerate LLM inference with speculative decoding · 7 sources tracked
Researchers are developing new methods to accelerate the inference speed of large language models (LLMs) through speculative decoding. DARTree and SPADE are two such approaches, with DARTree focusing on tree-based specu…
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New regularization technique combats suboptimal collapse in time series models
Researchers have introduced a new technique called Ground-Truth Neighborhood Regularization (GTN-R) to improve the performance of time series foundation models (TSFMs) when using reinforcement learning (RL) for post-tra…
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New framework personalizes federated adaptation for time-series models
Researchers have developed a personalized federated sparse adaptation framework for time-series foundation models (TSFMs), aiming to improve energy forecasting by addressing the non-IID nature of private, distributed me…
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LLM as Forecasting Planner framework integrates LLMs with TSFMs for improved forecasting
Researchers have developed a novel framework called LLM as Forecasting Planner (rc) that integrates large language models (LLMs) with time-series foundation models (TSFMs) for improved text-conditioned forecasting. This…
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Time series foundation models offer new capabilities for industrial AI
Time series foundation models are emerging as a critical advancement beyond traditional Large Language Models, particularly for industrial AI applications. These models are designed to learn general temporal patterns fr…
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Time Series Foundation Models show promise for wearable HRV forecasting
A new research paper explores the effectiveness of Time Series Foundation Models (TSFMs) for forecasting heart rate variability (HRV) from consumer wearable devices. The study evaluated TimesFM, Chronos, and MOIRAI agai…
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New framework unifies post-training methods for Time Series Foundation Models
A new research paper introduces a unifying framework for post-training methods applied to Time Series Foundation Models (TSFMs). The paper categorizes these methods into five types: parameter adaptation, context augment…
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New frameworks adapt foundation models for drought forecasting · 2 sources tracked
Researchers have developed novel inference-time frameworks, RGMR and SMR^2/MBB, to adapt pre-trained foundation models for regional climate forecasting, specifically for drought prediction. These methods allow for struc…
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New RMISC Corpus Enhances Time Series Foundation Models with Real-World Data · 2 sources tracked
Researchers have introduced RMISC, a large-scale, real-world corpus designed for training time series foundation models (TSFMs). This corpus, comprising approximately 200 datasets and 142 billion time points, aims to ad…
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Time Series Foundation Models show promise in electricity price forecasting
A new research paper evaluates the performance of Time Series Foundation Models (TSFMs) in electricity price forecasting, a domain characterized by complex temporal dependencies and distributional shifts. The study intr…
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Time Series Models Evaluated for US Influenza Forecasting
A new research paper evaluates various time series forecasting models for predicting seasonal influenza in the United States. The study found that a mixture-of-experts model, which combines multiple pretrained forecaste…
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New framework distills foundation models for specialized time-series forecasting
Researchers have developed a novel framework called Guard to distill knowledge from large, general-purpose foundation models (FMs) into lightweight, specialized time-series forecasters. This approach addresses the chall…
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Time series model benchmarks may hide critical failures, study finds
A new research paper published on arXiv highlights potential shortcomings in current benchmarks for time series foundation models (TSFMs). The study, focusing on traffic speed forecasting, reveals that aggregate metrics…