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 301–325 of 6748 papers

TitleStatusHype
Decoupling Local and Global Representations of Time SeriesCode1
Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin PlateCode1
Data Normalization for Bilinear Structures in High-Frequency Financial Time-seriesCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series ClassificationCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Arbitrage-free neural-SDE market modelsCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
Are we certain it's anomalous?Code1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive ModelingCode1
Anytime-valid off-policy inference for contextual banditsCode1
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
Deep Latent State Space Models for Time-Series GenerationCode1
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic HardwareCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite 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