Time series classifier recommendation by a meta-learning approach

Abanda, Amaia; Mori, Usue; Lozano, José A.

doi:10.1016/j.patcog.2022.108671

Cited by 7 publications

(14 citation statements)

References 52 publications

(66 reference statements)

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“…E ciently constructing the meta-feature dataset M from raw data remains a signi cant challenge for the recommendation system. A previous study [2] tackled this issue by using a random sampling method to…”

Section: Deep-learning Classi Er Recommendation In Dl-mtscrmentioning

confidence: 99%

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Suitable Deep Learning Classifier Recommendation for Multi-variate Time Series Classification

Gan

2024

Preprint

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Time series classification can be categorized into two main types: univariate and multivariate. The major difference between the two is that multivariate datasets have multiple dimensions, which makes them more intricate than univariate datasets. Selecting a suitable classifier for multivariate time series classification can be challenging, as it involves trying out several algorithms to handle the intricate data. While some studies have been conducted on automatically finding suitable classifiers for univariate datasets, there are currently no such studies for multivariate ones. To fill this gap, this paper introduces a recommendation system for deep-learning classifiers that are suitable for multivariate time series classification. The experiments demonstrate that this recommendation system can automatically identify the suitable deep-learning classifier for some multivariate time series datasets.

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Section: Deep-learning Classi Er Recommendation In Dl-mtscrmentioning

confidence: 99%

“…However, it is limited to univariate time series. Furthermore, out of the 24 candidate classi ers in [2], only 2 are deep-learning classi ers. Multivariate time series datasets are inherently more complex due to their multiple dimensions, making them more intricate than univariate datasets.…”

Section: Introductionmentioning

confidence: 99%

Suitable Deep Learning Classifier Recommendation for Multi-variate Time Series Classification

Gan

2024

Preprint

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“…It is an ensemble of a wide variety of base univariate time series classifiers. Its latest version uses as weak learners classifiers such as Shapelet Transform Classifier (STC) [3], Time Series Forest (TSF) [1], Contractable Bag of Symbolic-Fourier Approximation Symbols (CBOSS) [29], and Random Interval Spectral Ensemble (RISE) [25]. The notorious flaw of HIVE-COTE is its extremely high time complexity.…”

Section: ) Multiple Variables Handling With the Use Of Weak Learners ...mentioning

confidence: 99%

“…We find the Canonical Interval Forest (CIF) [27] classifier in this family of methods. It combines the TSF classifier [1] with an approach for feature extraction named Catch22 [26]. The feature extraction step ensures handling multiple variables when Catch22 samples intervals from different variables that are a base for features computation.…”

Section: ) Multiple Variables Handling Using Feature Extraction Proce...mentioning

confidence: 99%

“…• We envision that the data can be represented as multivariate time series made of a sequence of transactions where the variables describe "parameters" of transactions such as purchased items quantity and the variety of products (foods, drinks, condiments, etc. ), gross 1 https://www.ncr-hospitality.com/en/solutions/theft-detection-20/ value, discount, cash paid and change required. There are many variables, and time series may be very short.…”

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Variable-Length Multivariate Time Series Classification Using ROCKET: A Case Study of Incident Detection

Bier

Jastrzębska

Olszewski³

2022

IEEE Access

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Multivariate time series classification is a machine learning problem that can be applied to automate a wide range of real-world data analysis tasks. ROCKET proved to be an outstanding algorithm capable to classify time series accurately and quickly. The textbook variant of the multivariate time series classification problem assumes that time series to be classified are all of the same length, while in realworld applications this assumption not necessarily holds. The literature of this domain does not pay enough attention to data processing pipelines for variable-length time series. Thus, in this paper, we present a thorough analysis of three preprocessing pipelines that handle variable-length time series that need to be classified with a method that requires the data to be of equal length. These three methods are truncation, padding, and forecasting of missing value. Experiments conducted on benchmark datasets, showed that the recommended procedure involves padding. Forecasting ensures similar classification accuracy, but comes at a much higher computational cost. Truncation is not a viable option. Furthermore, in the paper, we present a novel domain of application of multivariate time series classification algorithms, that is incident detection in cash transactions. This area poses substantive challenges for automated model training procedures since the data is not only variable-length, but also heavily imbalanced. In the study, we list various incident types and present trained classifiers capable to aid human auditors in their daily work. INDEX TERMS classification, incident detection, multivariate time series, ROCKET, varying-length time series I. INTRODUCTIONT IME series classification has become a vital domain of machine learning. The multitude of exciting real-life applications drives the development of the field and inspires fruitful research that aim at delivering new approaches, improving the existing ones, and adapting them to new types of data. This paper focuses on the task of classifying multivariate time series of unequal lengths. That is, each multivariate series can have a different length.The study presented in this paper is related to our project that deals with incident detection in cash transaction data. The business context of the project is loss prevention in quick-service restaurant operations. In our context, an incident is when a cashier (server, employee) is under-ringing items, voiding items, or performing other intentional operations that cause money losses for the restaurant.Thanks to many years of successful cooperation with our clients in the quick-service restaurant industry, we have accumulated an adequate database of transactions, and we rely on human experts to conduct incident audits. The humanauditor-based loss prevention scheme employed at the moment is the cornerstone of the daily operations of our company. However, there is a pressing need to automate audit tasks.We want to build an automated classifier that detects operations in which a cashier intentionally mishandles t...

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