A multi-scale smoothing kernel for measuring time-series similarity

Troncoso, Alicia; Arias, Marta; Riquelme, José C.

doi:10.1016/j.neucom.2014.08.099

Cited by 10 publications

(2 citation statements)

References 30 publications

(35 reference statements)

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“…Another example is the kernel by Gaidon et al [92] for action recognition, in which the kernel is constructed on the auto-correlation of the series. There are also smoothing kernels that smooth the series with different techniques and then define the kernel for those smoothed representations [93][94][95] [96]. On the contrary, we will focus on those that define a kernel directly on the raw series.…”

Section: Definite Distance Kernelsmentioning

confidence: 99%

A review on distance based time series classification

Abanda

Mori

Lozano

2018

Data Min Knowl Disc

186

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Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to it simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.

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Section: Definite Distance Kernelsmentioning

confidence: 99%

A review on distance based time series classification

Abanda

Mori

Lozano

2018

Data Min Knowl Disc

186

View full text Add to dashboard Cite

show abstract

“…In addition, using multiple kernels could enhance the interpretability of the model and improve the generalization performance of the classifier. Reference [16] introduced a kernel function for time series data and used it for any data mining task that relies on similarity or distance measures. Bao et al [17] established multi-scale kernels approach through a multi-kernel learning framework.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Modeling Method for Aluminum Smelting Process Based on a Hybrid Strategy-Based Sparrow Search Algorithm

et al. 2022

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In the production of aluminum, the regenerative aluminum smelting process is an important part for energy efficiency and product quality. Aluminum liquid temperature is a significant variable in the aluminum smelting process, and it is costly to measure timely because it requires protective temperature sensor. To handle this problem, a kind of modeling framework which combine a mechanism model with multi-scale kernel technique is proposed. First, the mechanism model is built for the aluminum liquid temperature by the energy conservation law and heat transfer mechanism. Since the mechanism model is based on some assumptions, it often results in unknown variables. Thus, the multi-scale kernel technique is used to obtain the unknown variables. Finally, a hybrid temperature prediction model is built by combining the multi-scale kernel and the mechanism model. The parameter identification of the hybrid model is described as an optimization problem, and a hybrid strategy-based sparrow search algorithm (HSSA) is proposed to solve this problem. The experiment results show that HSSA has higher convergence accuracy and stronger global search ability than the original sparrow search algorithm (SSA), and the proposed hybrid model can correctly estimate the aluminum liquid temperature.

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Using hysteretic behaviour and hydrograph classification to identify hydrological function across the “hillslope–depression–stream” continuum in a karst catchment

Zhang

Chen

Zhang

et al. 2020

Hydrological Processes

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In cockpit karst landscapes, fluxes from upland areas contribute large volumes of water to low‐lying depressions and stream flow. Hydrograph hysteresis and similarity between monitoring sites is important for understanding the space–time variability of hydrologic responses across the “hillslope–depression–stream” continuum. In this study, the hysteretic feature of hydrographs was assessed by characterizing the loop‐like relationships between responses at upstream sites relative to subsurface discharge at the outlet of a small karst catchment. A classification of hydrograph responses based on the multi‐scale smoothing Kernel ‐derived distance classifies the hydrograph responses on the basis of similarities between hillslope and depression sites, and those at the catchment outlet. Results demonstrate that the temporal and spatial variability of hydrograph hysteresis and similarity between hillslope flow and outlet stream flow can be explained by the local heterogeneity of depression aquifer. Large depression storage deficits emerging in the highly heterogeneous aquifer produce strong hysteresis and multiple relationships of upstream hydrographs relative to the outlet subsurface discharge. In contrast, when depression storage deficits are filled during consecutive rainfall events, depression hydrographs at the high permeability sites are almost synchronous or exhibit a monotonous function with the hydrographs at the outlet. This reduced hydrograph hysteresis enhances preferential flow paths in fractured rocks and conduits that can accelerate the hillslope flow to the outlet. Therefore, classification of hydrograph similarities between any upstream sites and the catchment outlet can help to identify the dominant hydrological functions in the heterogeneous karst catchment.

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A multi-scale smoothing kernel for measuring time-series similarity

Cited by 10 publications

References 30 publications

A review on distance based time series classification

A review on distance based time series classification

A Hybrid Modeling Method for Aluminum Smelting Process Based on a Hybrid Strategy-Based Sparrow Search Algorithm

Using hysteretic behaviour and hydrograph classification to identify hydrological function across the “hillslope–depression–stream” continuum in a karst catchment

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