Online Learning with Radial Basis Function Networks
Financial time series are characterised by their nonstationarity and autocorrelation. Even if these time series are differenced, technically ensuring their stationarity, they experience regular covariate shifts and concept drifts. Against this backdrop, we combine feature representation transfer with sequential optimisation to provide multi-horizon returns forecasts. Our online learning rbfnet outperforms a random-walk baseline and several powerful batch learners. The rbfnets we formulate are naturally designed to measure the similarity between test samples and continuously updated prototypes that capture the characteristics of the feature space.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringContinual LearningTime SeriesTime Series AnalysisTransfer LearningSimilar Papers 제목 키워드 기반
Online ML-based Joint Channel Estimation and MIMO Decoding for Dynamic Channels
This paper presents an online method for joint channel estimation and decoding in massive MIMO-OFDM systems using complex-valued neural networks (CVNNs). The study evaluates the performance of various CVNNs, such as the …
Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions
Low-rank adaptation (LoRA) approximates the update of a pretrained weight matrix using the product of two low-rank matrices. However, standard LoRA follows an explicit-rank paradigm, where increasing model capacity requi…
Online Joint State Inference and Learning of Partially Unknown State-Space Models
A computationally efficient method for online joint state inference and dynamical model learning is presented. The dynamical model combines an a priori known, physically derived, state-space model with a radial basis fun…
State Space ModelsKolmogorov-Arnold Networks are Radial Basis Function Networks
This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions. Doing so leads to FastKAN, a much faster imple…
Kolmogorov-Arnold NetworksSolving Parametric PDEs with Radial Basis Functions and Deep Neural Networks
We propose the POD-DNN, a novel algorithm leveraging deep neural networks (DNNs) along with radial basis functions (RBFs) in the context of the proper orthogonal decomposition (POD) reduced basis method (RBM), aimed at a…