We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. The experiments we conduct show that there is a benefit to online transfer learning, in the form of radial basis function networks, beyond the sequential updating of recursive least-squares models. We show that the radial basis function networks, which make use of clustering algorithms to construct a kernel Gram matrix, are more beneficial than treating each training vector as separate basis functions, as occurs with kernel Ridge regression. We demonstrate quantitative procedures to determine the very structure of the radial basis function networks. Finally, we conduct experiments on the log returns of financial time series and show that the online learning models, particularly the radial basis function networks, are able to outperform a random walk baseline, whereas the offline learning models struggle to do so.

翻译：我们在财务时间序列中进行预测时,调查地物选择、非线性建模和在线学习的好处。我们考虑在线学习的连续和持续学习子类型。我们进行的实验表明,除了连续更新循环最小方形模型外,以无线电基功能网络的形式进行在线转移学习也有好处。我们显示,利用集群算法构建核心格拉姆矩阵的辐射基功能网络比将每个培训矢量作为单独的基础功能处理更为有益,就像内核脊回归那样。我们展示量化程序以确定辐射基功能网络的结构本身。最后,我们在财务时间序列的日志回报方面进行实验,并显示在线学习模型,特别是辐射基功能网络,能够超越随机行走基线,而离线学习模型则挣扎着这样做。