AR-Net: A simple autoregressive NN for #timeSeries
AR-Net, has 2 distinct advantages over its traditional counterpart:
* scales well to large orders, making it possible to estimate long-range dependencies (important in high-resolution monitoring applications, such as those in the data center domain);
* automatically selects and estimates the important coefficients of a sparse AR process, eliminating the need to know the true order of the AR process
To overcome the scalability challenge, they train a NN with #SGD to learn the AR (#autoregression) coefficients. AR-Net effectively learns near-identical weights as classic AR implementations & is equally good at predicting the next value of the time series.
Also, AR-Net automatically learns the relevant weights, even if the underlying data is generated by a noisy & extremely sparse AR process.
blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
paper: https://arxiv.org/abs/1911.03118
AR-Net, has 2 distinct advantages over its traditional counterpart:
* scales well to large orders, making it possible to estimate long-range dependencies (important in high-resolution monitoring applications, such as those in the data center domain);
* automatically selects and estimates the important coefficients of a sparse AR process, eliminating the need to know the true order of the AR process
To overcome the scalability challenge, they train a NN with #SGD to learn the AR (#autoregression) coefficients. AR-Net effectively learns near-identical weights as classic AR implementations & is equally good at predicting the next value of the time series.
Also, AR-Net automatically learns the relevant weights, even if the underlying data is generated by a noisy & extremely sparse AR process.
blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
paper: https://arxiv.org/abs/1911.03118