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 476–500 of 6748 papers

TitleStatusHype
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
Domain Adaptation for Time-Series Classification to Mitigate Covariate ShiftCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
Decoupling Local and Global Representations of Time SeriesCode1
Deep and Confident Prediction for Time Series at UberCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Adversarial Sticker: A Stealthy Attack Method in the Physical WorldCode1
Deep Counterfactual Estimation with Categorical Background VariablesCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
Deep Dynamic Factor ModelsCode1
A biologically plausible neural network for Slow Feature AnalysisCode1
Deep Isolation Forest for Anomaly DetectionCode1
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series PredictionCode1
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
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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