Clustering Time Series Utilizing a Dimension Hierarchical Decomposition Approach

2017 ◽  
pp. 247-261 ◽  
Author(s):  
Qiuhong Li ◽  
Peng Wang ◽  
Yang Wang ◽  
Wei Wang ◽  
Yimin Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Hierarchical Decomposition ◽  
Decomposition Approach

A hierarchical decomposition approach for multi-level building design optimization

2021 ◽  
Vol 44 ◽  
pp. 103272
Author(s):  
Yin Li ◽  
Nima Bonyadi ◽  
Ashleigh Papakyriakou ◽  
Bruno Lee
Keyword(s):  
Design Optimization ◽  
Building Design ◽  
Hierarchical Decomposition ◽  
Decomposition Approach ◽  
Multi Level ◽  
Level Building

scholarly journals A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 610 ◽  
Author(s):  
Xinghan Xu ◽  
Weijie Ren
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Back Propagation ◽  
Research Field ◽  
Chaotic Time Series ◽  
Good Prospect ◽  
Decomposition Approach ◽  
Model Based ◽  
Mode Decomposition

The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

scholarly journals Technical note: Decomposing a time series into independent trend, seasonal and random components

2018 ◽  
Author(s):  
Dongqin Yin ◽  
Hannah Slatford ◽  
Michael L. Roderick
Keyword(s):  
Time Series ◽  
Traditional Approach ◽  
Linear Trend ◽  
Moving Average ◽  
Climatic Variability ◽  
Original Data ◽  
Technical Note ◽  
Decomposition Approach ◽  
Random Components ◽  
Original Time

Abstract. Many time series observations in hydrology and climate show large seasonal variations and it has long been common practice to separate the original data into trend, seasonal and random components. We were interested in using that decomposition approach as a basis for understanding variability in hydro-climatic time series. For that purpose, it is desirable that the trend, seasonal and random components are independent so that the variance of the original time series equals the sum of the variances of the three components. We show that the resulting decomposition with the trend component traditionally estimated either as a linear trend or a moving average does not produce components that are independent. Instead we introduce the rarely adopted two-way ANOVA model into studies of hydro-climatic variability and define the trend as equal to the annual anomaly. This traditional approach produces a decomposition with three independent components. We then use global land precipitation data to demonstrate a simple application showing how this decomposition method can be used as a basis for comparing hydro-climatic variability. We anticipate that the three-part decomposition based on the two-way ANOVA approach will prove useful for future applications that seek to understand the space-time dimensions of hydro-climatic variability.

Extended hierarchical decomposition approach for the synthesis of biorefinery processes

2021 ◽  
Vol 166 ◽  
pp. 40-54 ◽  
Author(s):  
Su-Yi Tey ◽  
Sie Shing Wong ◽  
Jian An Lam ◽  
Norman Q.X. Ong ◽  
Dominic C.Y. Foo ◽  
...  
Keyword(s):  
Hierarchical Decomposition ◽  
Decomposition Approach
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