High-Frequency Observation and Characterization of the Marine Environment: Completion and Spectral Clustering of Multivariate Time Series Grassi K. 1,2,3 , Dezecache C. 2 , Phan T. T. H. 2,4 , Poisson-Caillault E. 2 , Bigand A. 2 , Lefebvre A 1 . 1 Ifremer, Laboratoire Environnement et Ressources, 62321, Boulogne sur Mer, France 2 LISIC, EA 4491, Université du Littoral Côte d’Opale, 62228 Calais, France. 3 WeatherForce, 31000 Toulouse, France 4 VNUA - Vietnam National University of Agriculture
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High-Frequency Observation and Characterization of
the Marine Environment: Completion and Spectral
Clustering of Multivariate Time Series
Grassi K.1,2,3, Dezecache C.2 , Phan T. T. H.2,4, Poisson-Caillault E.2, Bigand A.2, Lefebvre A1. 1 Ifremer, Laboratoire Environnement et Ressources, 62321, Boulogne sur Mer, France
2 LISIC, EA 4491, Université du Littoral Côte d’Opale, 62228 Calais, France. 3 WeatherForce, 31000 Toulouse, France 4VNUA - Vietnam National University of Agriculture
Source : [Dickey, 2003]
Low frequency (weekly or less)
High frequency (weekly or more)
Nested scales
2
REPHY/SRN
MAREL-Carnot
FerryBox
Source : [Dickey, 2003]
REPHY : RÉseau de surveillance du PHYtoplancton et des PHYcotoxines SRN : Suivi Régional des Nutriments
Classification independent from time and Fluorescence signal
13
3rd Spectral clustering
Phosphate
Time index
Sta
tes
Rare/Extreme events
Phosphate Correlation State 7 = 0.62
States
Intermittent Events : rare/extreme
Detection of environmental states
14
3rd Spectral clustering
Phosphate
Time index
Sta
tes
Rare/Extreme events
Correlation phosphates and turbidity
States
Detection of environmental states
Intermittent Events : rare/extreme
15
3rd Spectral clustering
Phosphate
Time index
Sta
tes
Rare/Extreme events
Correlation phosphates and turbidity
States
Phosphate Desorption
Detection of environmental states
New Phosphate stock available for phytoplancton
Intermittent Events : rare/extreme
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HF Databases
S1
S2
S3
S4
S5
S6
S7
S8
Spectral-C
Label
The protocol allowed to : - Optimize HF data processing - Define states in multi-parameters time series - Detect, identify and characterize this states
- Characterize events and extract label for frequent, rare or extreme events
CONCLUSIONS and PERSPECTIVES
Adding news data sources
DTWBI
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Multi Agent
Learning
HF Databases
Sat
in-situ
S1
S2
S3
S4
S5
S6
S7
S8
DTWBI Spectral-C ML/DL
Correspondence
Label/Data
ML/DL
ML/DL
∑
∑
∑ Sat Model + +
Label Machine Learning
Deep Learning
S1
S2
…
Sx
Prediction
Label Classification
system
S1
S2
…
Sx
S1
S2
…
Sx
New data
Majority
Vote
S1
S2
…
Sx
training dataset
CONCLUSIONS and PERSPECTIVES
Deep-Learning
Thank you for your attention
10/10/2018 18
The authors also want to acknowledge H2020 JERICO-Next for their financial contribution as well as the organizers.
This work has been partly funded by the French government and the region Hauts-de-France in the framework of the project CPER 2014-2020 MARCO
Kelly Grassi's PhD is funded by WeatherForce as part of its R & D program "Building an Initial State of the Atmosphere by Unconventional Data Aggregation".
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K-means Spectal-C Hierarchical-C
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Spectral Approach
Data segmentation
• Multi-sensor base
• No information
educational dataset: - A circle and a ball - 2000 points each
- States
Dimension 1 Dimension 1
Dim
ensi
on
2
Dim
ensi
on
2
K-means
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Projection of the
classification on
the data
sampled
initial space
Using the algorithm of the nearest neighbors K
to rank the initial base
Linearly separable data
K-means
N according to the gap
Algorithme de la classification spectrale
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Algorithme k-means
Criterion for minimizing intra-group distances
data
number of groups groups the barycentre of the group
K-means min J
𝑋
𝐾 µ𝑘
labels
1) Initialization of K centers
2) Assigning each point to its nearest center
3) New estimation of centers
4) Calculation of the criterion J, return to 2) if the criterion is not respected
with
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RESULTS
Rare and extreme events
Correlation : 0 .62
PCA States 7 (dim1/dim2)
PCA Regular series (dim1/dim2)
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1st Spectral clustering
Frequency of states by months
Sta
tes
Time index
Correlation of each parameter for a given cluster :
Temperature
s2
Scaled data
s1
Dynamics
Classification independently of time but Seasonal dynamics
Salinity Turbidity Temperature Dissolved Oxygen Nitrate Phosphate Silicate PAR Sea Level
Fig. DTW cost matrix showing the optimal matching path
Identification of similar sub-sequences
- Pre-selection of sequences based on global features over all times series available
- Minimization of the matching path based on DTW cost matrix
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Data gap problem
- Other methods are provided which aims at improving the limitation of DTW: • Derivative Dynamic Time Warping (DDTW) • Adaptive Feature Based Dynamic Time Warping (AFBDTW)
- Several functions are included to assess the similarity between time series: • Similarity • Root Mean Square Error (RMSE) • Normalized Mean Absolute Error (NMAE) • Fraction of Standard Deviation (FSD) • Fractional Bias (FB) • Fraction of data that satisfied smoothing amplitude cover (FA2)