SOTAVerified

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 776–800 of 6748 papers

TitleStatusHype
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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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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