Tables for paper "Time Series Forecasting Using Recurrent Neural Networks Based on Recurrent Sigmoid Piecewise Linear Neurons"

This document contains tables for the paper "Time Series Forecasting Using Recurrent Neural Networks Based on Recurrent Sigmoid Piecewise Linear Neurons". Description of each table in order (Table 1 to Table 3):RMSSE on 30 test time series (taken from the M5 competition dataset) of "C1-type networks" with different neuron types, specifically: LSTM, GRU, recurrent sigmoid piecewise linear (RSPL) neuron and recurrent sigmoid piecewise linear neuron without context normalization (RSPLWCN). C1-type network in the context of this paper means: a small neural network with 2 inputs, 1 recurrent layer with a 4-dimensional context vector.RMSSE on 30 test time series of "C2-type networks" for the same neuron types as above. C2-type network in the context of this paper means: a relatively big neural network with 4 inputs, 3 recurrent layers with an 8-dimensional context vector.Results from 2 previous tables, aggregated over all test time series.