arXiv:2606.01999v1 Announce Type: new Abstract: Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows. However, the benefit of increasing the window size is often simply attributed to capturing long-range dependencies, and broader discussion on how global forecasting models leverage input observations has been limited. In this paper, we show that forecasting groups of time series involves two objectives: (i) generative process identification (GPI), i.e., inferring the specific process generating the input sequence, and (ii) conditiona
The increasing complexity and computational demands of deep learning models for time series forecasting necessitate a deeper understanding of their underlying mechanisms, particularly as data volumes grow.
Understanding why time series models require long context windows can lead to more efficient and accurate AI models, reducing computational overhead and accelerating advancements in various predictive applications.
This research reframes the performance of time series models, introducing 'generative process identification' as a key objective, which can guide future model architecture and training strategies.
- · AI researchers
- · financial forecasting firms
- · supply chain optimizers
- · energy grid operators
- · inefficient time series model designs
- · companies relying on outdated forecasting methods
More robust and computationally efficient time series forecasting models are developed by focusing on generative process identification.
This leads to improved accuracy in predicting complex systems, from financial markets to climate patterns.
The enhanced predictive capabilities could drive new economic efficiencies and risk management strategies across multiple industries.
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Read at arXiv cs.LG