  In this study, we have shown autonomous long-term prediction with a
spintronic physical reservoir. Due to the short-term memory property of the
magnetization dynamics, non-linearity arises in the reservoir states which
could be used for long-term prediction tasks using simple linear regression for
online training. During the prediction stage, the output is directly fed to the
input of the reservoir for autonomous prediction. We employ our proposed
reservoir for the modeling of the chaotic time series such as Mackey-Glass and
dynamic time-series data, such as household building energy loads. Since only
the last layer of a RC needs to be trained with linear regression, it is well
suited for learning in real time on edge devices. Here we show that a skyrmion
based magnetic tunnel junction can potentially be used as a prototypical RC but
any nanomagnetic magnetic tunnel junction with nonlinear magnetization behavior
can implement such a RC. By comparing our spintronic physical RC approach with
energy load forecasting algorithms, such as LSTMs and RNNs, we conclude that
the proposed framework presents good performance in achieving high predictions
accuracy, while also requiring low memory and energy both of which are at a
premium in hardware resource and power constrained edge applications. Further,
the proposed approach is shown to require very small training datasets and at
the same time being at least 16X energy efficient compared to the sequence to
sequence LSTM for accurate household load predictions.
