KAN vs LSTM Performance in Time Series Forecasting

A new framework for Electric Vehicle (EV) charging forecasting has been proposed, addressing cybersecurity threats that compromise operational efficiency and grid stability. This framework employs a federated learning approach that integrates LSTM autoencoder-based anomaly detection, interpolation for data mitigation, and collaborative learning without centralized data aggregation.

A recent study has introduced a bidirectional Long Short-Term Memory (LSTM) neural network designed to classify light curves of transient astronomical objects using data from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC). The study reorganized the original fourteen object classes into five categories to mitigate class imbalance, achieving high performance in certain classifications while facing challenges with others.

A new paper presents a hybrid framework for portfolio optimization that combines Long Short-Term Memory (LSTM) forecasting with Proximal Policy Optimization (PPO) reinforcement learning. This innovative approach aims to enhance portfolio management by leveraging deep learning to predict market trends and dynamically adjust asset allocations across various financial instruments, including U.S. and Indonesian equities, U.S. Treasuries, and cryptocurrencies.

A new dataset named MultiBanAbs has been developed to facilitate Bangla abstractive summarization, comprising over 54,000 articles and summaries from diverse sources including blogs and newspapers. This initiative addresses the limitations of existing summarization systems that primarily focus on news articles, which often do not reflect the varied nature of real-world Bangla texts.

Recent research has introduced Liquid Time-Constant Networks (LTCs) and Closed-form Continuous-time Networks (CfCs) to model retinal ganglion cell activity in tiger salamanders, demonstrating lower mean absolute error (MAE) and faster convergence compared to traditional convolutional models and LSTMs.

A new framework for microgrid energy management has been developed, focusing on maintaining operational reliability and economic efficiency under cyberattack conditions. This approach integrates federated Long Short-Term Memory-based photovoltaic forecasting with a two-stage cascade false data injection attack detection system, addressing the challenges posed by malicious attacks on renewable energy forecasts.

A recent study introduces a framework for feature selection in supervised tabular data using Kolmogorov-Arnold Networks (KANs), which utilize splines to derive feature importance scores. The research compares KAN-based selection criteria against traditional methods like LASSO and Random Forest, demonstrating competitive performance in classification and regression tasks across various datasets.

The recent development of RadioKMoE introduces a knowledge-guided framework for radiomap estimation, combining Kolmogorov-Arnold Networks (KAN) with Mixture-of-Experts (MoE) to enhance wireless network management. This innovative approach addresses the challenges posed by complex radio propagation behaviors and environments, aiming for more accurate signal coverage predictions.