AutoEncoder for Interpolation

TL;DR

This paper proposes using AutoEncoders to perform simultaneous interpolation and denoising of noisy timeseries sensor data, addressing limitations of traditional methods in physical sciences.

Contribution

The novel approach combines interpolation and denoising in a single AutoEncoder model tailored for sensor timeseries data.

Findings

AutoEncoder effectively denoises sensor data.

AutoEncoder provides smooth interpolation of missing data points.

Method demonstrates improved data quality in real-world example.

Abstract

In physical science, sensor data are collected over time to produce timeseries data. However, depending on the real-world condition and underlying physics of the sensor, data might be noisy. Besides, the limitation of sample-time on sensors may not allow collecting data over all the timepoints, may require some form of interpolation. Interpolation may not be smooth enough, fail to denoise data, and derivative operation on noisy sensor data may be poor that do not reveal any high order dynamics. In this article, we propose to use AutoEncoder to perform interpolation that also denoise data simultaneously. A brief example using a real-world is also provided.