TimesFM
PulseAugur coverage of TimesFM — every cluster mentioning TimesFM across labs, papers, and developer communities, ranked by signal.
- 2026-08-24 product_launch Google Research released TimesFM 3.0, a new time-series foundation model. source
- 2026-08-11 product_launch Google has developed and released the TimesFM forecasting model. source
- 2025-09-23 research_milestone Google Research presented a new method for time-series foundation models to perform few-shot learning at inference time. source
6 day(s) with sentiment data
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New DES-JEPA framework learns shared vocabulary for high-dimensional event sequences
Researchers have introduced DES-JEPA, a novel framework designed to learn a shared discrete vocabulary for high-dimensional event sequences. Unlike existing temporal foundation models that rely on numerical spaces, DES-…
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New frameworks and benchmarks aim to advance AI-driven forecasting research
Researchers have developed TSFLab, a self-improving workbench designed to streamline time series forecasting (TSF) research. The platform addresses challenges such as the lack of accessible code for published TSF papers…
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MetaARIMA brings transfer learning to ARIMA time series forecasting
Researchers have developed MetaARIMA, a novel approach that applies transfer learning to the configuration of ARIMA models for time series forecasting. Unlike traditional methods that train each series independently, Me…
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MetaARIMA brings transfer learning to classical time series forecasting
Researchers have developed MetaARIMA, a novel approach that applies transfer learning to classical ARIMA time series forecasting models. Unlike previous methods that configure models independently for each series, MetaA…
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Google Research unveils 10 AI tools to amplify human ingenuity
Google Research showcased ten new AI-driven tools and initiatives at its Research@ event, aimed at amplifying human ingenuity across various fields. These advancements include enhanced geospatial analysis capabilities t…
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TimesFM model leads in electricity forecasting for university buildings
A new research paper published on arXiv explores the effectiveness of various machine learning models for day-ahead electricity forecasting in a university building. The study benchmarks traditional statistical methods,…
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New research on arXiv introduces advanced time series forecasting techniques · 9 sources tracked
Multiple research papers published on arXiv introduce novel methods for improving time series forecasting. SteerCast enhances decoder-only models by using a database of past data to guide predictions without retraining.…
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Foundation models show mixed results for pedestrian crowd forecasting
A new study published on arXiv evaluates the effectiveness of time-series foundation models (FMs) for pedestrian crowd count forecasting. The research compares seven different forecasting approaches, including tradition…
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Tabby: Open-Source Time Series Foundation Model Unveiled
Researchers have introduced Tabby, an open-source probabilistic time series foundation model designed for long contexts. Tabby utilizes an encoder-only patch Transformer architecture and was trained on a diverse corpus …
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Time-series AI models excel due to pretraining familiarity, not forecasting skill
A new study has revealed that pretraining familiarity, rather than genuine forecasting ability, significantly influences the performance of time-series foundation models. Researchers created a hold-out test set with dat…
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Google's TimesFM tested on e-commerce data via Kimi's Agent Swarm
A user tested Google Research's TimesFM model on a synthetic e-commerce dataset, utilizing the Agent Swarm platform from Kimi. The post provides details and results from this evaluation.
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Google unveils TimesFM-3 for multivariate time-series forecasting
Google Research has unveiled TimesFM-3, an AI model capable of multivariate forecasting. This model can analyze multiple time-series data points simultaneously, such as sales figures, weather patterns, and foot traffic,…
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Google AI releases TimesFM-3 for multivariate time series forecasting
Google AI has unveiled TimesFM-3, a 330 million parameter foundation model designed for multivariate time series forecasting. Unlike its predecessors, which were limited to univariate predictions, TimesFM-3 can jointly …
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Google Research releases TimesFM 3.0 for time-series forecasting
Google Research has released TimesFM 3.0, a new time-series foundation model built on a Stacked Mixing Transformer architecture. This model is designed for time-series forecasting and utilizes a decoder-only approach. I…
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Google's TimesFM challenges traditional forecasting methods
Google has developed TimesFM, a novel forecasting model that deviates from traditional workflows. The model's approach prompts a reevaluation of standard forecasting methodologies. This new tool offers a different persp…
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TS-Mob framework enhances time series models for human mobility prediction
Researchers have developed TS-Mob, a new framework designed to improve time series foundation models for predicting human mobility. This framework integrates geographic and social signals, computed from open data like p…
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Hybrid AI approach boosts stock prediction accuracy for foundation models
Researchers have developed a hybrid approach to improve the performance of frozen time series foundation models, specifically for high-frequency stock prediction. By combining neural correction architectures like AttnCo…
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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 (LAFP) that integrates large language models (LLMs) with time-series foundation models (TSFMs) for improved forecasting. This training-free …
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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 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…