GIFT-Eval
PulseAugur coverage of GIFT-Eval — every cluster mentioning GIFT-Eval across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New SATS method improves time series foundation model pretraining efficiency
Researchers have introduced SATS, a novel pretraining method for time series foundation models designed to handle heterogeneous datasets with varying sampling frequencies. SATS employs a scale-aware token alignment mech…
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TinyCast: Compact Zero-Shot Forecaster Achieves High Accuracy with Minimal Parameters
A new time series forecasting model named TinyCast has been introduced, featuring a compact design with only 146,505 parameters. This model utilizes a zero-parameter spectral detector to identify periodicity and dilated…
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TinyCast model achieves state-of-the-art zero-shot forecasting with minimal parameters
Researchers have introduced TinyCast, a novel zero-shot forecasting model that utilizes computed periodicity rather than learning it, making it highly efficient with only 146,505 parameters. This model outperforms exist…
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New research tackles training and evaluation challenges for time series foundation models
Two new research papers explore challenges in training and evaluating time series foundation models (TSFMs). The first paper introduces ORBIT, a novel training paradigm designed to control distribution properties like d…
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Google Research releases TimesFM 2.5 for zero-shot time-series forecasting
Google Research has released TimesFM 2.5, an open-source foundation model for time-series forecasting. This model, with 200 million parameters and a context window of up to 16,384 points, can predict future trends witho…
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TiRex-2 model advances multivariate time series forecasting with recurrent xLSTM design
Researchers have introduced TiRex-2, a novel recurrent foundation model based on xLSTM architecture designed for multivariate time series forecasting. This model addresses limitations of existing Transformer-based appro…
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FlowState Model Achieves Sampling-Rate-Equivariant Time-Series Forecasting
Researchers have introduced FlowState, a new time-series foundation model designed for enhanced adaptability and efficiency. Unlike previous transformer-based models, FlowState utilizes a state space model encoder paire…
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New MoE frameworks enhance time series forecasting efficiency and accuracy
Researchers have developed new Mixture-of-Experts (MoE) frameworks for time series forecasting that aim to improve efficiency and accuracy. AME-TS uses structure-guided routing to align expert specialization with tempor…
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Toto 2.0 models show scaling benefits in time series forecasting
Researchers have introduced Toto 2.0, a suite of five open-weight time series forecasting models that demonstrate the effectiveness of scaling foundation models. The models, trained using a single recipe, show improved …