Using a Neural Network Autoencoder Framework for Time Series Interpolation
Loading...
Authors
Callaghan, Evan James
Date
2024-09-25
Type
thesis
Language
eng
Keyword
Time series , Interpolation , Neural networks , Autoencoder
Alternative Title
Abstract
This thesis focuses on constructing a robust and effective time series data interpolation method; one that makes no assumptions about the underlying structure of the series and outperforms current state-of-the-art techniques. For this task, we turn to advanced neural network models. In this work, we detail the development, implementation, and testing of a neural network-based autoencoder model designed for time series interpolation. This method aims to extract key elements from the sequential input data and recover the missing data points by leveraging patterns found within complete sections of the time series.
In the development stage, we discuss the proposed algorithm and summarize each step. In the implementation stage, we outline the Python and R code development, wrapping up with a proof-of-concept example. Lastly, in the testing stage, we perform a series of interpolation simulations to evaluate performance. Simulation results show several instances where our technique provides improved performance compared to other methods, most notably, the Hybrid Wiener Interpolator (HWI).
Following the evaluation of our newly developed method, we apply adaptations to the algorithm, inspired by the HWI, to create a hybrid approach. These adaptations involve an extra data preprocessing step in which we detect trend and periodic components from the input series. Simulations of the hybrid approach show significant improvements over the original implementation for highly structured time series, and remain consistent for input time series with few structural components.
Description
Citation
Publisher
License
Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International