Improving time series modeling by decomposing and analyzing stochastic and deterministic influences

Authors:
Ricardo AraúJo Rios;Rodrigo Fernandes De Mello
Affiliations:
-;-
Venue:
Signal Processing
Year:
2013

Citing 8
Cited 0

Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
An Introduction to Wavelets

IEEE Computational Science & Engineering
A self-organising network that grows when required

Neural Networks - New developments in self-organizing maps
Frequency estimation of undamped exponential signals using genetic algorithms

Computational Statistics & Data Analysis
Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation

Artificial Intelligence in Medicine
Noise reduction method for chaotic signals based on dual-wavelet and spatial correlation

Expert Systems with Applications: An International Journal
Universal FIR MMSE filtering

IEEE Transactions on Signal Processing
Multiscale finite impulse response modeling

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper proposes a new approach to improve time series modeling by considering stochastic and deterministic influences. Assuming such influences are present in observations, a first decomposition step is required to split them into two components: one stochastic and another deterministic. As second step, models are adjusted on each component and combined to form a hybrid model improving time series analysis. The proposed approach considers the Empirical Mode Decomposition method and a Recurrence Plot-based measurement to decompose and assess stochastic and deterministic influences. Experiments confirmed improvements in time series modeling.