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.
翻译:金融时间序列的特征是其非常态和自动关系。即使这些时间序列存在差异,在技术上确保了它们的固定性,它们也经历着经常的共变变化和概念漂移。在此背景下,我们把特征代表转换与顺序优化结合起来,以提供多正数回报预测。我们的网上学习 rbfnet 超越了随机行走基线和几个强大的批量学习者。 我们设计的 rbfnet 自然设计来测量测试样品和不断更新的反映特征空间特性的原型之间的相似性。