arXiv:2511.18613v2 Announce Type: replace-cross Abstract: This study presents a controlled comparison of baseline Kolmogorov-Arnold Networks (KAN), implemented via PyKAN, and Long Short-Term Memory (LSTM) networks for the forecasting of stochastic, non-stationary financial time series. The two architectures are assessed in terms of predictive accuracy, computational efficiency, and interpretability, with accuracy measured by the Root Mean Square Error (RMSE) in normalised feature space. Under a direct multi-output forecasting protocol, LSTM attains clearly superior accuracy across all tested p
The proliferation of new AI architectures like KANs necessitates constant benchmarking against established methods like LSTMs to understand their practical utility.
This study offers a comparative performance analysis of a novel AI architecture (KAN) against a widely used one (LSTM) for a specific application (time series forecasting), contributing to the ongoing evaluation of AI model capabilities.
The finding suggests that for financial time series forecasting, LSTMs currently maintain superior accuracy over baseline KANs, indicating continued relevance of established methods in certain domains.
- · LSTM users
- · Time series forecasting sector
- · Baseline KAN developers
- · Early KAN adopters
Further research will likely focus on improving KAN architectures to match or surpass LSTM performance in specific applications.
Investment in KAN-based solutions for financial time series forecasting might be tempered until significant performance improvements are demonstrated.
The broader AI community may continue to prioritize proven architectures for critical, accuracy-sensitive applications while new models mature.
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Read at arXiv cs.AI