Search Results for author:
Found 16 papers, 12 papers with code
Elastic Similarity and Distance Measures for Multivariate Time Series
Elastic similarity and distance measures are a class of similarity measures that can compensate for misalignments in the time axis of time series data.
Tight lower bounds for Dynamic Time Warping
Due to DTW's high computation time, lower bounds are often employed to screen poor matches.
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning
For this task, we survey a wide range of techniques available for handling missing values, self-supervised training and pseudo-likelihood training, and adapt them to a suite of algorithms that are suitable for the task.
Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification
This paper explores how to best handle seasonal drift in the specific context of news article categorization (or classification/tagging), where seasonal drift is overwhelmingly the main type of drift present in the data, and for which the data are high-dimensional.
Time Series Extrinsic Regression
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label.
Monash University, UEA, UCR Time Series Extrinsic Regression Archive
We refer to this problem as Time Series Extrinsic Regression (TSER), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series.
Time series classification for varying length series
Research into time series classification has tended to focus on the case of series of uniform length.
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification
We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification.
Proximity Forest: An effective and scalable distance-based classifier for time series
We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.
Elastic bands across the path: A new framework and methods to lower bound DTW
One of the key time series classification algorithms, the nearest neighbor algorithm with DTW distance (NN-DTW) is very expensive to compute, due to the quadratic complexity of DTW.
On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error
These hypotheses lead to the concept of the sweet path, a path through the 3-d space of alternative drift rates, forgetting rates and bias/variance profiles on which generalization error will be minimized, such that slow drift is coupled with low forgetting and low bias, while rapid drift is coupled with fast forgetting and low variance.
Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
The main result of this paper is to show that improved parameter estimation allows BNCs to outperform leading learning methods such as Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.
Characterizing Concept Drift
This supports the development of the first comprehensive set of formal definitions of types of concept drift.
Deep Broad Learning - Big Models for Big Data
For some learning tasks there is power in learning models that are not only Deep but also Broad.
Skopus: Mining top-k sequential patterns under leverage
It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest.
