KAN vs LSTM Performance in Time Series Forecasting

• URL: http://arxiv.org/abs/2511.18613v1
• Date: Sun, 23 Nov 2025 21:09:58 GMT
• Title: KAN vs LSTM Performance in Time Series Forecasting
• Abstract summary: This paper compares Kolmogorov-Arnold Networks (KAN) and Long Short-Term Memory networks (LSTM) for forecasting non-deterministic stock price data.<n>LSTM demonstrates substantial superiority across all tested prediction horizons.<n>Findings support LSTM adoption for practical financial forecasting while suggesting that continued research into specialised KAN architectures may yield future improvements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper compares Kolmogorov-Arnold Networks (KAN) and Long Short-Term Memory networks (LSTM) for forecasting non-deterministic stock price data, evaluating predictive accuracy versus interpretability trade-offs using Root Mean Square Error (RMSE).LSTM demonstrates substantial superiority across all tested prediction horizons, confirming their established effectiveness for sequential data modelling. Standard KAN, while offering theoretical interpretability through the Kolmogorov-Arnold representation theorem, exhibits significantly higher error rates and limited practical applicability for time series forecasting. The results confirm LSTM dominance in accuracy-critical time series applications while identifying computational efficiency as KANs' primary advantage in resource-constrained scenarios where accuracy requirements are less stringent. The findings support LSTM adoption for practical financial forecasting while suggesting that continued research into specialised KAN architectures may yield future improvements.

Related papers

• PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting [0.40105987447353786]
  This study introduces PRICE, a structured approach for adapting Large Language Models to short-term Bitcoin price forecasting.<n>Built on a 4-bit quantized LLaMA-3 8B model, PRICE investigates how fine-tuning, numerical representation, prompting, inference, and decoding jointly influence performance.<n>Price achieves the lowest forecasting errors on both validation and test sets while maintaining robust performance across evaluation periods.
  arXiv  Detail & Related papers  (2026-09-04T15:01:38Z)
• LEAF: A Living Benchmark for Event-Augmented Forecasting [78.94019460534442]
  LEAF is the first living benchmark for event-augmented forecasting tasks.<n>We evaluate state-of-the-art proprietary and open-weight LLMs.<n>We find that LLMs achieve better performance on equities they confidently identify as more predictable.
  arXiv  Detail & Related papers  (2026-05-09T03:17:59Z)
• TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems [52.91956121737963]
  We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems.<n>We propose a systematic multi-agent framework that utilizes an iterative verification loop to synthesize numerically grounded reasoning traces.<n>Our evaluation confirms that this reasoning is causally effective; useful for evaluation; and prompting LLMs with our generated traces significantly improves forecasting accuracy.
  arXiv  Detail & Related papers  (2026-04-07T03:04:45Z)
• Observationally Informed Adaptive Causal Experimental Design [55.998153710215654]
  We propose Active Residual Learning, a new paradigm that leverages the observational model as a foundational prior.<n>This approach shifts the experimental focus from learning target causal quantities from scratch to efficiently estimating the residuals required to correct observational bias.<n> Experiments on synthetic and semi-synthetic benchmarks demonstrate that R-Design significantly outperforms baselines.
  arXiv  Detail & Related papers  (2026-03-04T06:52:37Z)
• Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking [51.56484100374058]
  We evaluate whether a fully automatic, purely feedback-driven ESN can serve as a competitive alternative to widely used statistical forecasting methods.<n>Forecast accuracy is measured using MASE and sMAPE and benchmarked against simple benchmarks like drift and seasonal naive and statistical models.
  arXiv  Detail & Related papers  (2026-02-03T16:01:22Z)
• Km-scale dynamical downscaling through conformalized latent diffusion models [45.94979929172337]
  Dynamical downscaling is crucial for deriving high-resolution meteorological fields from coarse-scale simulations.<n>Generative Diffusion models (DMs) have recently emerged as powerful data-driven tools for this task.<n>However, DMs lack finite-sample guarantees against overconfident predictions, resulting in miscalibrated grid-point-level uncertainty estimates.<n>We tackle this issue by augmenting the downscaling pipeline with a conformal prediction framework.
  arXiv  Detail & Related papers  (2025-10-15T08:41:36Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• A Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models [32.56983347493999]
  Cross-frequency transfer learning (CFTL) has emerged as a popular framework for curating large-scale time series datasets to pre-train foundation forecasting models (FFMs)<n>Although CFTL has shown promise, current benchmarking practices fall short of accurately assessing its performance.<n>This shortcoming stems from many factors: an over-reliance on small-scale evaluation datasets; inadequate treatment of sample size when computing summary statistics; reporting of suboptimal statistical models; and failing to account for non-negligible risks of overlap between pre-training and test datasets.
  arXiv  Detail & Related papers  (2025-09-23T18:19:50Z)
• Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles [7.787518725874443]
  Time series foundation models (TSFMs) have shown strong generalization and zero-shot capabilities for time series forecasting, anomaly detection, classification, and imputation.<n>This paper investigates a suite of statistical and ensemble-based enhancement techniques to improve robustness and accuracy.
  arXiv  Detail & Related papers  (2025-08-18T04:06:26Z)
• Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction [1.1534313664323632]
  This research introduces a new ML framework designed to enhance the robustness of out-of-sample hydrocarbon production forecasting.<n>Utilizing historical data from the Volve (wells PF14, PF12) and Norne (well E1H) oil fields, this study investigates the efficacy of various predictive algorithms.
  arXiv  Detail & Related papers  (2025-08-12T19:14:46Z)
• Transforming Credit Risk Analysis: A Time-Series-Driven ResE-BiLSTM Framework for Post-Loan Default Detection [12.68085308605668]
  Prediction of post-loan default is an important task in credit risk management.<n>This study introduces a ResE-BiLSTM model, using a sliding window technique.<n>It is evaluated on 44 independent cohorts from the extensive Freddie Mac US mortgage dataset.
  arXiv  Detail & Related papers  (2025-08-01T08:13:14Z)
• Kolmogorov-Arnold Networks-based GRU and LSTM for Loan Default Early Prediction [13.227020042685687]
  The study introduces two innovative architectures, GRU-KAN and LSTM-KAN, which merge Kolmogorov-Arnold Networks (KAN) with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks.<n>The proposed model achieves a prediction accuracy of over 92% three months in advance and over 88% eight months in advance, significantly outperforming existing baselines.
  arXiv  Detail & Related papers  (2025-07-18T06:20:41Z)
• Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training [57.03005244917803]
  Large language models (LLMs) often fail on out-of-distribution (OOD) samples due to spurious correlations acquired during pre-training.<n>Here, we aim to mitigate such spurious correlations through causality-aware post-training (CAPT)<n> Experiments on the formal causal inference benchmark CLadder and the logical reasoning dataset PrOntoQA show that 3B-scale language models fine-tuned with CAPT can outperform both traditional SFT and larger LLMs on in-distribution (ID) and OOD tasks.
  arXiv  Detail & Related papers  (2025-06-11T06:30:28Z)
• Topology-Aware Conformal Prediction for Stream Networks [68.02503121089633]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework.<n>Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Learning representations with end-to-end models for improved remaining useful life prognostics [64.80885001058572]
  The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. We propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. We will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T16:45:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.