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It is shown in this study that it is possible to leverage the predictive power of Large Language Models (LLMs) to infer causal relations between only two sequences of events, which provides better results than standard causal discovery algorithms on several time series data, even though these data were converted into s...
Empirical results show that RCBNB-MB systematically outperforms baseline approaches in accurately detecting regime changes and their associated causal graphs, positioning it as a robust and versatile framework for non-stationary time series analysis.
This work reviews the main architectures, pre-training strategies, and optimization methods underpinning foundation models for zero-shot time series forecasting, and investigates post-pre-training fine-tuning of selected foundation models to enhance their performance on specific datasets.
AdaRDiff is proposed, a generalized differencing approach that uses learnable weights to simplify the series through weighted differencing with previous time instants, and attains state-of-the-art forecast accuracy across eight benchmarks spanning electricity, weather, traffic, and energy, at negligible parameter cost.
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