Feature Extraction for Functional Time Series: Theory and Application to NIR Spectroscopy Data

06/02/2021
by   Yang Yang, et al.
0

We propose a novel method to extract global and local features of functional time series. The global features concerning the dominant modes of variation over the entire function domain, and local features of function variations over particular short intervals within function domain, are both important in functional data analysis. Functional principal component analysis (FPCA), though a key feature extraction tool, only focus on capturing the dominant global features, neglecting highly localized features. We introduce a FPCA-BTW method that initially extracts global features of functional data via FPCA, and then extracts local features by block thresholding of wavelet (BTW) coefficients. Using Monte Carlo simulations, along with an empirical application on near-infrared spectroscopy data of wood panels, we illustrate that the proposed method outperforms competing methods including FPCA and sparse FPCA in the estimation functional processes. Moreover, extracted local features inheriting serial dependence of the original functional time series contribute to more accurate forecasts. Finally, we develop asymptotic properties of FPCA-BTW estimators, discovering the interaction between convergence rates of global and local features.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

11/25/2020

Functional Principal Component Analysis of Cointegrated Functional Time Series

Functional principal component analysis (FPCA) has played an important r...
06/26/2021

Functional Classwise Principal Component Analysis: A Novel Classification Framework

In recent times, functional data analysis (FDA) has been successfully ap...
10/07/2011

A Face Recognition Scheme using Wavelet Based Dominant Features

In this paper, a multi-resolution feature extraction algorithm for face ...
01/14/2020

Nonparametric Trend Estimation in Functional Time Series with Application to Annual Mortality Rates

Here, we address the problem of trend estimation for functional time ser...
07/25/2021

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes ass...
10/21/2021

An Empirical Evaluation of Time-Series Feature Sets

Solving time-series problems with features has been rising in popularity...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.