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
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
Our approach uses a new form of time series average, the ShapeDTW Barycentric Average.
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
andrewlai61616/npsr
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NeurIPS 2023
In this paper, we propose a framework for unsupervised time series anomaly detection that utilizes point-based and sequence-based reconstruction models.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data.
Evaluating Explanation Methods for Multivariate Time Series Classification
mlgig/Evaluating-Explanation-Methods-for-MTSC
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In many applications classification alone is not enough, we often need to classify but also understand what the model learns (e. g., why was a prediction given, based on what information in the data).
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach
agustdd/floss
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To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain.
Network Traffic Classification based on Single Flow Time Series Analysis
Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication.
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI
visual-xai-for-time-series/time-series-xai-perturbation-analysis
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This paper provides an in-depth analysis of using perturbations to evaluate attributions extracted from time series models.
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
FITS: Modeling Time Series with $10k$ Parameters
vewoxic/fits
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In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis.
