Time series extrinsic regression

Tan, Chang Wei; Bergmeir, Christoph; Petitjean, François; Webb, Geoffrey I.

doi:10.1007/s10618-021-00745-9

Cited by 60 publications

(40 citation statements)

References 52 publications

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“…The models obtained similar but not identical results on the three last datasets. This behavior was also observed on the original benchmark results [20]. The implementation of the TSER benchmark models can be found on GitHub 3 .…”

Section: E Tser Benchmarksupporting

confidence: 67%

Aircraft Numerical “Twin”: A Time Series Regression Competition

Pavao

Guyon

Stéphane

et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

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This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft "numerical twins", which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient.

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Section: E Tser Benchmarksupporting

confidence: 67%

Aircraft Numerical “Twin”: A Time Series Regression Competition

Pavao

Guyon

Stéphane

et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

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“…The application of ADTW in the many other types of task to which DTW is often applied remains a productive direction for future investigation. These include similarity search [11], regression [12], clustering [13], anomaly and outlier detection [14], motif discovery [15], forecasting [16], and subspace projection [17]. One issue that will need to be addressed in each of these domains is how best to tune the amercing penalty ω, especially if a task does not have objective criteria by which utility may be judged.…”

Section: Discussionmentioning

confidence: 99%

“…DTW is a foundational technique for a wide range of time series data analysis tasks, including similarity search [11], regression [12], clustering [13], anomaly and outlier detection [14], motif discovery [15], forecasting [16], and subspace projection [17].…”

Section: Background and Related Workmentioning

confidence: 99%

Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Herrmann¹,

Webb²

2021

Preprint

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Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDTW) variants, are time series distances with a wide range of applications.They minimize the cost of non-linear alignments between series. CDTW and WDTW have been introduced because DTW is too permissive in its alignments.However, CDTW uses a crude step function, allowing unconstrained flexibility within the window, and none beyond it. WDTW's multiplicative weight is relative to the distances between aligned points along a warped path, rather than being a direct function of the amount of warping that is introduced. In this paper, we introduce Amerced Dynamic Time Warping (ADTW), a new, intuitive, DTW variant that penalizes the act of warping by a fixed additive cost. Like CDTW and WDTW, ADTW constrains the amount of warping.However, it avoids both abrupt discontinuities in the amount of warping allowed and the limitations of a multiplicative penalty. We formally introduce ADTW, prove some of its properties, and discuss its parameterization. We show on a simple example how it can be parameterized to achieve an intuitive outcome, and demonstrate its usefulness on a standard time series classification benchmark.We provide a demonstration application in C++ [1].

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“…Tensorflow was used to implement the models. ResNet, FCN, and Inception-Time implementations are adapted from [40]. We also used the pyts package to implement recurrence plots and the Piecewise Aggregate Approximation algorithm.…”

Section: Methodsmentioning

confidence: 99%

Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

Ojeda

Peramo²,

Solano

2021

Learning and Analytics in Intelligent Systems

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We present a framework for multivariate nonlinear time series forecasting that utilizes phase space image representations and deep learning. Recurrence plots (RP) are a phase space visualization tool used for the analysis of dynamical systems. This approach takes advantage of recurrence plots that are used as input image representations for a class of deep learning algorithms called convolutional neural networks. We show that by leveraging recurrence plots with optimal embedding parameters, appropriate representations of underlying dynamics are obtained by the proposed autoregressive deep learning model to produce forecasts.

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Time series extrinsic regression

Cited by 60 publications

References 52 publications

Aircraft Numerical “Twin”: A Time Series Regression Competition

Aircraft Numerical “Twin”: A Time Series Regression Competition

Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

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