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 626–650 of 6748 papers

TitleStatusHype
A Generalised Signature Method for Multivariate Time Series Feature ExtractionCode1
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFIDCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Ensembles of Localised Models for Time Series ForecastingCode1
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting EpidemicsCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
Explainable Deep Convolutional Candlestick LearnerCode1
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time IntervalsCode1
Explaining Time Series Predictions with Dynamic MasksCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Aligning Time Series on Incomparable SpacesCode1
Are we certain it's anomalous?Code1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Calibration of Google Trends Time SeriesCode1
Financial Time Series Data Processing for Machine LearningCode1
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic HardwareCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
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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