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Found 6 papers, 1 papers with code
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
Hence, this paper presents a first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art.
A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data
To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features.
A Dictionary-based approach to Time Series Ordinal Classification
Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results.
Monotonic classification: an overview on algorithms, performance measures and data sets
Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making.
Time series clustering based on the characterisation of segment typologies
These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process.
OCAPIS: R package for Ordinal Classification And Preprocessing In Scala
The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems.
