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 276–300 of 6748 papers

TitleStatusHype
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
Arbitrage-free neural-SDE market modelsCode1
Decoupling Local and Global Representations of Time SeriesCode1
Deep and Confident Prediction for Time Series at UberCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
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
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Anytime-valid off-policy inference for contextual banditsCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series DataCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Data Normalization for Bilinear Structures in High-Frequency Financial Time-seriesCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
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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