New time series decomposition method and its application on machinery fault diagnosis

2007 ◽  
Vol 43 (08) ◽  
pp. 171 ◽  
Author(s):  
Yong LÜ
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Decomposition Method ◽  
Time Series Decomposition ◽  
Machinery Fault Diagnosis ◽  
New Time

scholarly journals STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones

2021 ◽  
Vol 5 (1) ◽  
pp. 42
Author(s):  
Zuokun Ouyang ◽  
Philippe Ravier ◽  
Meryem Jabloun
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Decomposition Method ◽  
Combination Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Time Series Decomposition

This paper aims at comparing different forecasting strategies combined with the STL decomposition method. STL is a versatile and robust time series decomposition method. The forecasting strategies we consider are as follows: three statistical methods (ARIMA, ETS, and Theta), five machine learning methods (KNN, SVR, CART, RF, and GP), and two versions of RNNs (CNN-LSTM and ConvLSTM). We conduct the forecasting test on six horizons (1, 6, 12, 18, and 24 months). Our results show that, when applied to monthly industrial M3 Competition data as a preprocessing step, STL decomposition can benefit forecasting using statistical methods but harms the machine learning ones. Moreover, the STL-Theta combination method displays the best forecasting results on four over the five forecasting horizons.

scholarly journals Trends in Optical Span Loss Detected Using the Time Series Decomposition Method

2020 ◽  
Vol 38 (18) ◽  
pp. 5026-5035
Author(s):  
Banti Laure M. Yameogo ◽  
Douglas W. Charlton ◽  
David Doucet ◽  
Christian Desrosiers ◽  
Maurice O'Sullivan ◽  
...  
Keyword(s):  
Time Series ◽  
Decomposition Method ◽  
Time Series Decomposition
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