@inproceedings{73732bca990845ca809345038208e0cf,
title = "Time series forecasting via noisy channel reversal",
abstract = "Developing a precise understanding of the dynamic behavior of time series is crucial for the success of forecasting techniques. We introduce a novel communication-theoretic framework for modeling and forecasting time series. In particular, the observed time series is modeled as the output of a noisy communication system with the input as the future values of time series. We use a data-driven probabilistic approach to estimate the unknown parameters of the system which in turn is used for forecasting. We also develop an extension of the proposed framework together with a filtering algorithm to account for the noise and heterogeneity in the quality of time series. Experimental results demonstrate the effectiveness of this approach.",
keywords = "Communication theoretic data analytics, Filtering, Noise, Regression",
author = "Pejman Khadivi and Prithwish Chakraborty and Ravi Tandon and Naren Ramakrishnan",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 ; Conference date: 17-09-2015 Through 20-09-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/MLSP.2015.7324330",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Deniz Erdogmus and Serdar Kozat and Jan Larsen and Murat Akcakaya",
booktitle = "2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015",
}