Abstract: Methods, systems and techniques for multivariate time series forecasting are provided. A dataset is obtained that corresponds to a multivariate time series data for a multivariate time series forecasting task. A particular machine learning architecture is used for the forecasting using an artificial neural network and deep learning. The machine learning architecture includes an autoencoder configured and trained on itself moved forward in time to generate autoencoder layers to analyse seasonality and co-variance information of the multivariate input dataset in a future time frame and an autoregressor to generate autoregressor layers to analyse trend information of the multivariate input dataset in a future time frame; and a layer merger for merging the one or more autoregressor layers and one or more autoencoder layers to form a set of merged layers representative of a multivariate time series forecast using the machine learning model in the future time frame.

Abstract: Computational systems and methods are provided to automatically assess residual characteristics of an existing machine learning model to identify and determine suboptimal pockets and augmentation strategies. A computing system, device and method for optimizing a machine learning model for performing predictions is provided. The computing device performs sub-optimal pocket identification on an existing machine learning algorithm by residual analysis to calculate an error. The computing device utilizes the residual as a target for an ensemble tree model and automatically generates a set of interpretable rules from the tree based ensemble model that contribute to the suboptimal pockets. The rules indicating relationships between features and interactions as well as values for the sub-optimal pockets. The computing device determines optimizations for improving the machine learning model based on the interpretable computer-implemented rules.

Abstract: An all digital modem includes a clock divider for generating four waveforms at a desired transmission frequency but mutually phase shifted by 90 degrees, a first microprocessor programmed to receive digital data at its input, to convert the data into dibits and to select as a modem output one of the four waveforms phase shifted 90 degrees with respect to each other. To demodulate incoming signals, a zero crossing detector detects zero crossings of an incoming modulated signal, a second microprocessor coupled receives the zero crossing information along with parity information and converts the information into dibits, and a third microprocessor receives dibit outputs from the second microprocessor and converts the dibits into a serial stream of data, at essentially constant frequency.