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 251–300 of 6748 papers

TitleStatusHype
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
Deep reconstruction of strange attractors from time seriesCode1
Deep Switching State Space Model (DS^3M) for Nonlinear Time Series Forecasting with Regime SwitchingCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Deep Counterfactual Estimation with Categorical Background VariablesCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
Deep Dynamic Factor ModelsCode1
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
Deep Autoregressive Models with Spectral AttentionCode1
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
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
Deep Isolation Forest for Anomaly DetectionCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Decoupling Local and Global Representations of Time SeriesCode1
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
Arbitrage-free neural-SDE market modelsCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series ClassificationCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
Deep and Confident Prediction for Time Series at UberCode1
Deep Latent State Space Models for Time-Series GenerationCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Anytime-valid off-policy inference for contextual banditsCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series DataCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Show:102550
← PrevPage 6 of 135Next →
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