Hi everyone.
Welcome to our presentation of ESPRESSO: an Entropy and shape aware time-series segmentation
for processing heterogeneous sensor data.
I am Shohreh Deldari from RMIT University.
Today We are surrounded by a huge number of sensors collecting Multi-variate time series
data constantly.
They can be used for different applications such as medical purposes, human activity recognition
tasks, Smart home and smart cities, electricity distribution planning and so on.
Annotation and extracting meaningful segments from this huge amount of data is challenging,
expensive and sometimes infeasible.
That's why Self-supervised and unsupervised methods are a hot topic these days.
Segmentation will help us to extract meaningful parts from unlabelled data and make it ready
for further processing.
We define time series segmentation as a change point detection technique which looks for
changes in properties of the given time series to find the most homogeneous segments that
can represent a specific state of the system.
Existing methods can be categorized based on the features they consider to detect changes.
The statistical differences between time intervals have commonly been measured in many parametric
and non-parametric methods.
Some of these methods are based on assumptions on the distribution of data;
while other try to calculate the relative ratio of distributions of subseuenct segments
. Or focus on the information gain of segments.
or the dependency and correlation between segments
The other category of segmentation techniques focuses on the temporal pattern and shape-based
features .
Each of the mentioned methods is applicable to only one type of datasets.
For example, the shape-based method can detect changes in signals with repeated patterns;
while the statistical methods are able to detect sudden changes in the statistical features
or distribution of adjacent segments, but not gradual changes.
However, input data can be any combination of these categories.
According to these characteristics we categorized the input data into 4 different types and
chose 7 multivariate datasets covering different types of categories.
we showed that our method is applicable across different type of inputs.
we proposed a hybrid approach and combined shape base and statistical characteristics
To make ESPRESSO applicable to a wide range of datasets.
First, we extract the most similar patterns using Matrix Profile,
Then as an enhancement to FLOSS as a shape-based segmentation method, we find the chain of
similar patterns for each subsequence based on the Euclidean distance as the similarity
measure.
The dotted arcs show the newly added pairs in the chain.
Next, we assign a weight to each extract pair of similar subsequence based on the similarity
measure and temporal distance.
In this picture, each arc shows a pair of most similar patterns.
the Thicker arcs show higher similarity and lower temporal distance.
The main idea here is that repeated patterns relate to the same class segments, and hence,
occur within close temporal proximity.
Therefore, we wouldn't expect to see many arcs crossing the segment boundaries.
Time steps with less density of crossing arcs will be selected and segment boundary candidates.
We burrowed the idea of information gain segmentation from IGTS.
However, instead of dynamic programming or top-down search, we use a greedy search across
extracted candidates to find the segments with the minimum entropy.
At each step, we select the boundary candidate which minimize the given entropy function.
We compared our proposed method against 4 other baselines across 7 multivariate datasets
with different size, dimensionality, sensor type and applications.
And evaluated the results using 3 metrics
First of all, we showed that the performance of our method is less dependent on the only
input parameter which is the length of subsequenc.
Here we compared the sensitivity of ESPRESSO and each of the benchmarks across PAMAP and
Hand gesture dataset.
This table shows the average value of RMSE and F-score across a range of subsequences
and subjects within each dataset.
We also compared the effectiveness of each method according to the category of input.
Here you can see FLOSS as a shape-based method cannot capture segments in datasets which
contains not-repeating patterns very well.
On the other hand, statistic-based methods do not perform very well in continuous datasets
with gradual change.
But ESPRESSO outperforms all other methods in all types of input data no matter the data
includes continuous changes or repeated patterns or not.
An additional study is performed to investigate the contribution of both the temporal shape
and entropy-based components of ESPRESSO independently across different types of datasets.
To conclude we highlite the main contributions of our work:
- ESPRESSO integrates temporal shape and entropy based properties of multi-dimensional time-series.
- This hybrid model enables ESPRESSO to be applicable across a broad range of inputs.
- ESPRESSO only depends on one parameter which can be selected with minimal risk
- An embedded channel ranking has been utilised in ESPRESSO to make segmentation more robust
to noisy and/or irrelevant channels We also explored two real-world case studies
on work routine deviation and changes in emotion.
Thank you for watching this video.