Time Series Analysis
1879 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
Self-Supervised Learning for Time Series: Contrastive or Generative?
In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series.
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series
This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models.
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
To this end, we explore the impact of discrete, learnt, time series data representations that enable generalist, cross-domain training.
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network
To address these challenges, we propose a Multi-modal Time Series Analysis Model Based on Spiking Neural Network (MTSA-SNN).
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
kimmeen/time-llm
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Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.
Timer: Transformers for Time Series Analysis at Scale
thuml/Timer
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Continuous progresses have been achieved as the emergence of large language models, exhibiting unprecedented ability in few-shot generalization, scalability, and task generality, which is however absent in time series models.
Large Language Models for Time Series: A Survey
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning
Meanwhile, we design a supervised task to learn more robust representations and facilitate the contrastive learning process.
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.
