Compact Autoregressive Network

Stationary and Integrated Autoregressive Neural Network Processes

We consider autoregressive neural network (AR-NN) processes driven by additive noise and demonstrate that the characteristic roots of the shortcuts-the standard conditions from linear time-series analysis-determine the stochastic behavior of the overall AR-NN process. If all the characteristic roots are outside the unit circle, then the process is ergodic and stationary. If at least one charact...

Compact Signatures for Network Coding

Network coding offers increased throughput and improved robustness to random faults in completely decentralized networks. Since it does not require centralized control, network coding has been suggested for routing packets in ad-hoc networks, for content distribution in P2P file systems, and for improving the efficiency of large-scale data dissemination over the Internet. In contrast to traditi...

A comparative study of autoregressive neural network hybrids

Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this a...

Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming

In Time-Division-Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to variation of spatial signature vectors in vehicular scenarios. To mitigate this, downlink beams must be adjusted according to changing propagation dynamics. This can be achieved by modeling spatial signature vectors in the uplink period and then predicting the...

On the Stationarity of Autoregressive Neural Network Models