Pejman Khadivi, Naren Ramakrishnan


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.



Naren Ramakrishnan


Pejman Khadivi

Publication Details

Date of publication:
September 17, 2015
IEEE International Workshop on Machine Learning for Signal Processing (MLSP)