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Technical Brief

Neural Probabilistic Forecasting of Symbolic Sequences with Long Short-Term Memory

[+] Author and Article Information
Michael Hauser

Graduate Research Assistant, Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802
mzh190@psu.edu

Yiwei Fu

Graduate Research Assistant, Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802
yxf118@psu.edu

Shashi Phoha

Senior Scientist, Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802
sxp26@arl.psu.edu

Dr. Asok Ray

Distinguished Professor, Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802
axr2@psu.edu

1Corresponding author.

ASME doi:10.1115/1.4039281 History: Received April 17, 2017; Revised January 08, 2018

Abstract

This paper makes use of long short-term memory (LSTM) neural networks for forecasting probability distributions of time series in terms of discrete symbols that are quantized from real-valued data. The developed framework formulates the forecasting problem into a probabilistic paradigm. The proposed method is different from standard formulations (e.g. autoregressive moving average) of time series modeling. The main advantage of formulating the problem in the symbolic setting is that density predictions are obtained without any significantly restrictive assumptions (e.g. second order statistics). The efficacy of the proposed method has been demonstrated by forecasting probability distributions on chaotic time series data collected from a laboratory-scale experimental apparatus. Three neural architectures are compared, each with 100 different combinations of symbol-alphabet size and forecast length, resulting in a comprehensive evaluation of their relative performances.

Copyright (c) 2018 by ASME
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