Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods.

The large amount of data accumulated so far on the dynamics of the COVID-19 outbreak has allowed assessing the accuracy of forecasting methods in retrospect. This work compares several basic time series analysis methods, including machine learning methods, for forecasting the number of confirmed cases for some days ahead. Year-long data for all regions of Russia has been used from the Yandex DataLens platform. As a result, accuracy estimates for these basic methods have been obtained for Russian regions and Russia as a whole, in dependence on the forecasting horizon. The best basic models for forecasting for 14 days are exponential smoothing and ARIMA, with an error of 11-19% by the MAPE metric for the latest part of the course of the epidemic. The accuracies obtained can be considered as baselines for more complex prospective models.

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20) х (LiNbO3-y )100-х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.