8,791 research outputs found
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
We consider the problem of estimating the remaining useful life (RUL) of a
system or a machine from sensor data. Many approaches for RUL estimation based
on sensor data make assumptions about how machines degrade. Additionally,
sensor data from machines is noisy and often suffers from missing values in
many practical settings. We propose Embed-RUL: a novel approach for RUL
estimation from sensor data that does not rely on any degradation-trend
assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes
a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to
generate embeddings for multivariate time series subsequences. The embeddings
for normal and degraded machines tend to be different, and are therefore found
to be useful for RUL estimation. We show that the embeddings capture the
overall pattern in the time series while filtering out the noise, so that the
embeddings of two machines with similar operational behavior are close to each
other, even when their sensor readings have significant and varying levels of
noise content. We perform experiments on publicly available turbofan engine
dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL
outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad
Adaptive, locally-linear models of complex dynamics
The dynamics of complex systems generally include high-dimensional,
non-stationary and non-linear behavior, all of which pose fundamental
challenges to quantitative understanding. To address these difficulties we
detail a new approach based on local linear models within windows determined
adaptively from the data. While the dynamics within each window are simple,
consisting of exponential decay, growth and oscillations, the collection of
local parameters across all windows provides a principled characterization of
the full time series. To explore the resulting model space, we develop a novel
likelihood-based hierarchical clustering and we examine the eigenvalues of the
linear dynamics. We demonstrate our analysis with the Lorenz system undergoing
stable spiral dynamics and in the standard chaotic regime. Applied to the
posture dynamics of the nematode our approach identifies
fine-grained behavioral states and model dynamics which fluctuate close to an
instability boundary, and we detail a bifurcation in a transition from forward
to backward crawling. Finally, we analyze whole-brain imaging in
and show that the stability of global brain states changes with oxygen
concentration.Comment: 25 pages, 16 figure
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
The increasing use of electronic forms of communication presents new
opportunities in the study of mental health, including the ability to
investigate the manifestations of psychiatric diseases unobtrusively and in the
setting of patients' daily lives. A pilot study to explore the possible
connections between bipolar affective disorder and mobile phone usage was
conducted. In this study, participants were provided a mobile phone to use as
their primary phone. This phone was loaded with a custom keyboard that
collected metadata consisting of keypress entry time and accelerometer
movement. Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We propose an
end-to-end deep architecture based on late fusion, named DeepMood, to model the
multi-view metadata for the prediction of mood scores. Experimental results
show that 90.31% prediction accuracy on the depression score can be achieved
based on session-level mobile phone typing dynamics which is typically less
than one minute. It demonstrates the feasibility of using mobile phone metadata
to infer mood disturbance and severity.Comment: KDD 201
One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms
Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital
for efficient application deployment and resource provisioning. However, the
heterogeneous application patterns, variable infrastructure performance, and
frequent deployments in MT-ECP pose significant challenges for accurate and
efficient workload prediction. Clustering-based methods for dynamic MT-ECP
modeling often incur excessive costs due to the need to maintain numerous data
clusters and models, which leads to excessive costs. Existing end-to-end time
series prediction methods are challenging to provide consistent prediction
performance in dynamic MT-ECP. In this paper, we propose an end-to-end
framework with global pooling and static content awareness, DynEformer, to
provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously
designed global pooling and information merging mechanisms can effectively
identify and utilize global application patterns to drive local workload
predictions. The integration of static content-aware mechanisms enhances model
robustness in real-world scenarios. Through experiments on five real-world
datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP
and provided a unified end-to-end prediction scheme for MT-ECP.Comment: 10 pages, 10 figure
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