Time-series analysis including forecasting is essential in a range of fields from finance to engineering. However, long-term forecasting is difficult, particularly for cases where the underlying models and parameters are complex and unknown. Neural networks can effectively process features in temporal units and are attractive for such purposes. Reservoir computing, in particular, can offer efficient temporal processing of recurrent neural networks with a low training cost, and is thus well suited to time-series analysis and forecasting tasks. Here, we report a reservoir computing hardware system based on dynamic tungsten oxide (WO_x) memristors that can efficiently process temporal data. The internal short-term memory effects of the WO_x memristors allow the memristor-based reservoir to nonlinearly map temporal inputs into reservoir states, where the projected features can be readily processed by a linear readout function. We use the system to experimentally demonstrate two standard benchmarking tasks: isolated spoken-digit recognition with partial inputs, and chaotic system forecasting. A high classification accuracy of 99.2% is obtained for spoken-digit recognition, and autonomous chaotic time-series forecasting has been demonstrated over the long term.

从金融到工程的各个领域，包括预测在内的时间序列分析都是必不可少的。但是，很难进行长期预测，尤其是对于基础模型和参数复杂且未知的情况。神经网络可以以时间单位有效地处理特征，并且对于此类目的具有吸引力。尤其是，储层计算可以以较低的培训成本对循环神经网络进行有效的时间处理，因此非常适合于时间序列分析和预测任务。在这里，我们报告基于动态氧化钨（WO _x）忆阻器的储层计算硬件系统，该系统可以有效地处理时间数据。WO _x的内部短期记忆效应忆阻器允许基于忆阻器的储层将时间输入非线性映射到储层状态，在此可以通过线性读出函数轻松处理投影特征。我们使用该系统实验性地演示了两个标准的基准测试任务：具有部分输入的孤立语音识别和混沌系统预测。语音数字识别的分类准确率高达99.2％，长期以来已经证明了自主的混沌时间序列预测。