Time Series Analysis
1880 papers with code • 3 benchmarks • 20 datasets
Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.
( Image credit: Autoregressive CNNs for Asynchronous Time Series )
Benchmarks
These leaderboards are used to track progress in Time Series Analysis
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Libraries
Use these libraries to find Time Series Analysis models and implementationsDatasets
Speech Commands
PhysioNet Challenge 2012
EigenWorms
TSSB
Hurricane
Subtasks
Latest papers
PatchAD: Patch-based MLP-Mixer for Time Series Anomaly Detection
emorzz1g/patchad
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In this study, we introduce PatchAD, a novel multi-scale patch-based MLP-Mixer architecture that leverages contrastive learning for representational extraction and anomaly detection.
Invertible Solution of Neural Differential Equations for Analysis of Irregularly-Sampled Time Series
To handle the complexities of irregular and incomplete time series data, we propose an invertible solution of Neural Differential Equations (NDE)-based method.
The Rise of Diffusion Models in Time-Series Forecasting
This survey delves into the application of diffusion models in time-series forecasting.
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series.
One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors
Despite the impressive achievements of pre-trained models in the fields of natural language processing (NLP) and computer vision (CV), progress in the domain of time series analysis has been limited.
A projected nonlinear state-space model for forecasting time series signals
shimazaki/pnlss
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Learning and forecasting stochastic time series is essential in various scientific fields.
Raising the ClaSS of Streaming Time Series Segmentation
Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes.
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
dl4mhealth/comet
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NeurIPS 2023
The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets.
A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
kimmeen/time-llm
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In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
