Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
On the parameterization and initialization of diagonal state space models
State space models (SSM) have recently been shown to be very effective as a deep learning
layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers …
layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers …
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations
(NDEs) are popular families of deep learning models for time-series data, each with unique …
(NDEs) are popular families of deep learning models for time-series data, each with unique …
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
Liquid structural state-space models
A proper parametrization of state transition matrices of linear state-space models (SSMs)
followed by standard nonlinearities enables them to efficiently learn representations from …
followed by standard nonlinearities enables them to efficiently learn representations from …
TEST: Text prototype aligned embedding to activate LLM's ability for time series
This work summarizes two strategies for completing time-series (TS) tasks using today's
language model (LLM): LLM-for-TS, design and train a fundamental large model for TS data; …
language model (LLM): LLM-for-TS, design and train a fundamental large model for TS data; …
Unsupervised feature based algorithms for time series extrinsic regression
Abstract Time Series Extrinsic Regression (TSER) involves using a set of training time series
to form a predictive model of a continuous response variable that is not directly related to the …
to form a predictive model of a continuous response variable that is not directly related to the …
Graph neural networks for multivariate time series regression with application to seismic data
Abstract Machine learning, with its advances in deep learning has shown great potential in
analyzing time series. In many scenarios, however, additional information that can …
analyzing time series. In many scenarios, however, additional information that can …
Time-series classification with SAFE: Simple and fast segmented word embedding-based neural time series classifier
N Tabassum, S Menon, A Jastrzębska - Information Processing & …, 2022 - Elsevier
Dictionary-based classifiers are an essential group of approaches in the field of time series
classification. Their distinctive characteristic is that they transform time series into segments …
classification. Their distinctive characteristic is that they transform time series into segments …