1,938 research outputs found
Neural activity classification with machine learning models trained on interspike interval series data
The flow of information through the brain is reflected by the activity
patterns of neural cells. Indeed, these firing patterns are widely used as
input data to predictive models that relate stimuli and animal behavior to the
activity of a population of neurons. However, relatively little attention was
paid to single neuron spike trains as predictors of cell or network properties
in the brain. In this work, we introduce an approach to neuronal spike train
data mining which enables effective classification and clustering of neuron
types and network activity states based on single-cell spiking patterns. This
approach is centered around applying state-of-the-art time series
classification/clustering methods to sequences of interspike intervals recorded
from single neurons. We demonstrate good performance of these methods in tasks
involving classification of neuron type (e.g. excitatory vs. inhibitory cells)
and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep
states) on an open-access cortical spiking activity dataset
QUANT: A Minimalist Interval Method for Time Series Classification
We show that it is possible to achieve the same accuracy, on average, as the
most accurate existing interval methods for time series classification on a
standard set of benchmark datasets using a single type of feature (quantiles),
fixed intervals, and an 'off the shelf' classifier. This distillation of
interval-based approaches represents a fast and accurate method for time series
classification, achieving state-of-the-art accuracy on the expanded set of 142
datasets in the UCR archive with a total compute time (training and inference)
of less than 15 minutes using a single CPU core.Comment: 26 pages, 20 figure
TEASER: Early and Accurate Time Series Classification
Early time series classification (eTSC) is the problem of classifying a time
series after as few measurements as possible with the highest possible
accuracy. The most critical issue of any eTSC method is to decide when enough
data of a time series has been seen to take a decision: Waiting for more data
points usually makes the classification problem easier but delays the time in
which a classification is made; in contrast, earlier classification has to cope
with less input data, often leading to inferior accuracy. The state-of-the-art
eTSC methods compute a fixed optimal decision time assuming that every times
series has the same defined start time (like turning on a machine). However, in
many real-life applications measurements start at arbitrary times (like
measuring heartbeats of a patient), implying that the best time for taking a
decision varies heavily between time series. We present TEASER, a novel
algorithm that models eTSC as a two two-tier classification problem: In the
first tier, a classifier periodically assesses the incoming time series to
compute class probabilities. However, these class probabilities are only used
as output label if a second-tier classifier decides that the predicted label is
reliable enough, which can happen after a different number of measurements. In
an evaluation using 45 benchmark datasets, TEASER is two to three times earlier
at predictions than its competitors while reaching the same or an even higher
classification accuracy. We further show TEASER's superior performance using
real-life use cases, namely energy monitoring, and gait detection
Benchmarking Multivariate Time Series Classification Algorithms
Time Series Classification (TSC) involved building predictive models for a
discrete target variable from ordered, real valued, attributes. Over recent
years, a new set of TSC algorithms have been developed which have made
significant improvement over the previous state of the art. The main focus has
been on univariate TSC, i.e. the problem where each case has a single series
and a class label. In reality, it is more common to encounter multivariate TSC
(MTSC) problems where multiple series are associated with a single label.
Despite this, much less consideration has been given to MTSC than the
univariate case. The UEA archive of 30 MTSC problems released in 2018 has made
comparison of algorithms easier. We review recently proposed bespoke MTSC
algorithms based on deep learning, shapelets and bag of words approaches. The
simplest approach to MTSC is to ensemble univariate classifiers over the
multivariate dimensions. We compare the bespoke algorithms to these dimension
independent approaches on the 26 of the 30 MTSC archive problems where the data
are all of equal length. We demonstrate that the independent ensemble of
HIVE-COTE classifiers is the most accurate, but that, unlike with univariate
classification, dynamic time warping is still competitive at MTSC.Comment: Data Min Knowl Disc (2020
Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms
The state-of-the-art in time series classification has come a long way, from
the 1NN-DTW algorithm to the ROCKET family of classifiers. However, in the
current fast-paced development of new classifiers, taking a step back and
performing simple baseline checks is essential. These checks are often
overlooked, as researchers are focused on establishing new state-of-the-art
results, developing scalable algorithms, and making models explainable.
Nevertheless, there are many datasets that look like time series at first
glance, but classic algorithms such as tabular methods with no time ordering
may perform better on such problems. For example, for spectroscopy datasets,
tabular methods tend to significantly outperform recent time series methods. In
this study, we compare the performance of tabular models using classic machine
learning approaches (e.g., Ridge, LDA, RandomForest) with the ROCKET family of
classifiers (e.g., Rocket, MiniRocket, MultiRocket). Tabular models are simple
and very efficient, while the ROCKET family of classifiers are more complex and
have state-of-the-art accuracy and efficiency among recent time series
classifiers. We find that tabular models outperform the ROCKET family of
classifiers on approximately 19% of univariate and 28% of multivariate datasets
in the UCR/UEA benchmark and achieve accuracy within 10 percentage points on
about 50% of datasets. Our results suggest that it is important to consider
simple tabular models as baselines when developing time series classifiers.
These models are very fast, can be as effective as more complex methods and may
be easier to understand and deploy
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