基于径向基函数网络的在线学习
Online Learning with Radial Basis Function Networks
摘要：
金融时间序列具有非平稳性和自相关性。即使这些时间序列是差分的，从技术上保证了它们的平稳性，它们也会经历规则的协变量偏移和概念漂移。在此背景下，我们将特征表示转移与顺序优化相结合，以提供多水平回报预测。我们的在线学习rbfnet优于随机游走基线和几个强大的批量学习器。我们制定的rbfnets自然是为了测量测试样本和不断更新的原型之间的相似性，这些原型捕获了特征空间的特征。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
