Recent advances in deep learning have driven rapid progress in time series forecasting, yet many state-of-the-art models continue to struggle with robust performance in real-world applications, even when they achieve strong results on standard benchmark datasets. This persistent gap can be attributed to the blackbox nature of deep learning architectures and the inherent limitations of current evaluation frameworks, which frequently lack the capacity to provide clear, quantitative insights into the specific strengths and weaknesses of different models, thereby complicating the selection of appropriate models for particular forecasting scenarios. To address these issues, we propose a synthetic data-driven evaluation paradigm, SynTSBench, that systematically assesses fundamental modeling capabilities of time series forecasting models through programmable feature configuration. Our framework isolates confounding factors and establishes an interpretable evaluation system with three core analytical dimensions: (1) temporal feature decomposition and capability mapping, which enables systematic evaluation of model capacities to learn specific pattern types; (2) robustness analysis under data irregularities, which quantifies noise tolerance thresholds and anomaly recovery capabilities; and (3) theoretical optimum benchmarking, which establishes performance boundaries for each pattern type--enabling direct comparison between model predictions and mathematical optima. Our experiments show that current deep learning models do not universally approach optimal baselines across all types of temporal features.

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A.2 ExpertRollouts The expert rollouts consist of acollection of HDF5 files, one file per clip. A.3 HostingPlan The link to the dataset can be found on the project website. The dataset website also includes the policies we trained in Section 5, i.e., the multi-clip tracking policies, RL-trained taskpolicies, andtheGPTpolicy. Training clip experts to track long clips is potentially slow and laborious, so wefollowMerel etal.[2019]bydividing Each expert is a neural network with three hidden layers, 1024 neurons in each hidden layer, and thetanh activation.

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ARacknowledgesfundingfroman Adobe Faculty Research Award, andan NSFDMS 1916320 grant.

We introduce Flamingo, a family of Visual Language Models (VLM) with this ability.