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Time Series Analysis

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 )

Papers

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Showing 751–800 of 6748 papers

TitleStatusHype
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Multi-Time Attention Networks for Irregularly Sampled Time SeriesCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
Multi-Variate Time Series Forecasting on Variable SubsetsCode1
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural NetworksCode1
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic DataCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Automated Evolutionary Approach for the Design of Composite Machine Learning PipelinesCode1
Network of Tensor Time SeriesCode1
Network Traffic Classification based on Single Flow Time Series AnalysisCode1
Neural Rough Differential Equations for Long Time SeriesCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood RiskCode1
Neural ODE ProcessesCode1
Neural ODEs as Feedback Policies for Nonlinear Optimal ControlCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Changing Fashion CulturesCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
Calibration of Google Trends Time SeriesCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Nonlinear proper orthogonal decomposition for convection-dominated flowsCode1
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
Can LLMs Understand Time Series Anomalies?Code1
NTS-NOTEARS: Learning Nonparametric DBNs With Prior KnowledgeCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And ForecastingCode1
Online Change Point Detection in Molecular Dynamics With Optical Random FeaturesCode1
Online Metro Origin-Destination Prediction via Heterogeneous Information AggregationCode1
On the adoption of abductive reasoning for time series interpretationCode1
On the performance of deep learning models for time series classification in streamingCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEsCode1
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention NetworksCode1
Parameter Efficient Deep Probabilistic ForecastingCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric ClassificationCode1
Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Automatic Change-Point Detection in Time Series via Deep LearningCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified