Test Time Learning for Time Series Forecasting
Keywords: Time Series Forecasting, Test-Time Training, Mamba, Expressive Hidden States, Modern CNN
TL;DR: Test-Time Learning Applied To Long Time Series Forecasting
Abstract: We propose the use of Test-Time Training (TTT) modules in a cascade architecture to enhance performance in long-term time series forecasting. Through extensive experiments on standard benchmark datasets, we demonstrate that TTT modules consistently outperform state-of-the-art models, including Mamba-based TimeMachine, particularly in scenarios involving extended sequence and prediction lengths. Our results show significant improvements, especially on larger datasets such as Electricity, Traffic, and Weather, underscoring the effectiveness of TTT in capturing long-range dependencies. Additionally, we explore various convolutional architectures within the TTT framework, showing that convolutional blocks as hidden layer architectures can achieve competitive results.
Primary Area: learning on time series and dynamical systems
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Submission Number: 12519
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