---
title: Kolmogorov-Arnold Networks for Time Series Analysis
url: https://www.ml-quant.com/papers/ssrn/5217399/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5217399
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5217399
featured: 2025-04-16
citations: unknown
topic: Econometrics & Forecasting
---


# Kolmogorov-Arnold Networks for Time Series Analysis

Kolmogorov-Arnold Networks (KANs) are a strong alternative to MultiLayer Perceptron (MLP) for time series analysis and forecasting, as outlined in the survey.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5217399
- Identifier: SSRN 5217399
- Released: 2025-04-14
- First featured: Quant Letter No. 93 (2025-04-16): https://www.ml-quant.com/issues/2025-04-16/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting](https://www.ml-quant.com/papers/arxiv/2504.02518/): The study introduces a faster, more accurate online model for predicting electricity prices.
- [The heterogeneous causal effects of the EU's Cohesion Fund](https://www.ml-quant.com/papers/arxiv/2504.13223/): A study measures the impact of the EU's Cohesion Fund on regional output and investment, showing its peak effect within the first seven years, particularly in poorer regions.
- [Cross-Modal Temporal Fusion for Financial Market Forecasting](https://www.ml-quant.com/papers/arxiv/2504.13522/): The article presents Cross-Modal Temporal Fusion (CMTF), a new transformer-based system that uses diverse financial data to enhance the precision of financial market predictions.
- [Hierarchical sparse Bayesian multitask learning for disease prediction in pooled microbiome studies](https://www.ml-quant.com/papers/arxiv/2502.02552/): The article discusses a hierarchical Bayesian multitask learning model for binary classification learning, which effectively predicts human health status using microbiome profiles.