Time Series Classification
242 papers with code • 51 benchmarks • 17 datasets
Time Series Classification is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.
Source: Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
Benchmarks
These leaderboards are used to track progress in Time Series Classification
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Libraries
Use these libraries to find Time Series Classification models and implementationsDatasets
SHAPES
UCI Machine Learning Repository
PhysioNet Challenge 2012
US Economy
Latest papers with no code
Early detection of disease outbreaks and non-outbreaks using incidence data
In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur.
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification.
DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc.
InceptionTime vs. Wavelet -- A comparison for time series classification
Neural networks were used to classify infrasound data.
LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification.
Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model
To address this challenge, we propose CrossTimeNet, a novel cross-domain SSL learning framework to learn transferable knowledge from various domains to largely benefit the target downstream task.
Advancing Time Series Classification with Multimodal Language Modeling
In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm.
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
We show that the model performs with high reliability and efficiency on the online CPD problem ($\sim$98\% and $\sim$97\% area under precision-recall curve respectively).
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings
In this paper, we propose a novel temporal pooling method with diverse perspective learning: Selection over Multiple Temporal Poolings (SoM-TP).
