Time Series Analysis
1884 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 with no code
From Generalization Analysis to Optimization Designs for State Space Models
A State Space Model (SSM) is a foundation model in time series analysis, which has recently been shown as an alternative to transformers in sequence modeling.
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images
Fusion methods can process different images, modalities, and tasks and are expected to be robust and adaptive to various types of images (e. g., spectral images, classification maps, and elevation maps) and scene complexities.
Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks
The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance.
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more.
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models.
Deep Learning for Satellite Image Time Series Analysis: A Review
Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years.
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications, and thus investigation of deep learning for HOIs has become a valuable agenda for the data mining and machine learning communities.
A Survey on Deep Learning and State-of-the-art Applications
However, the studies mostly focused on the types of deep learning models and convolutional neural network architectures, offering limited coverage of the state-of-the-art of deep learning models and their applications in solving complex problems across different domains.
A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery
In this study, we contribute a summary of experimental data on microglial cell counts in the penumbra following ischemic stroke induced by middle cerebral artery occlusion (MCAO) in mice and compile available data sets into a single set suitable for time series analysis.
