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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 of 6748 papers

TitleStatusHype
Changing Fashion CulturesCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence CaseCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series DataCode1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
Differentiable Divergences Between Time SeriesCode1
Data-driven discovery of intrinsic dynamicsCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Discovering Nonlinear Relations with Minimum Predictive Information RegularizationCode1
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
InceptionTime: Finding AlexNet for Time Series ClassificationCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic ForecastingCode1
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI ModellingCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
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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