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 576–600 of 6748 papers

TitleStatusHype
Data Normalization for Bilinear Structures in High-Frequency Financial Time-seriesCode1
catch22: CAnonical Time-series CHaracteristicsCode1
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series ClassificationCode1
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Deep and Confident Prediction for Time Series at UberCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
Deep Dynamic Factor ModelsCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Deep Latent State Space Models for Time-Series GenerationCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Adaptive Conformal Predictions for Time SeriesCode1
Deep Learning Statistical ArbitrageCode1
Deep Semi-Supervised Learning for Time Series ClassificationCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
Deep Switching Auto-Regressive Factorization:Application to Time Series ForecastingCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
DeepVATS: Deep Visual Analytics for Time SeriesCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
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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