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 1001–1025 of 6748 papers

TitleStatusHype
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
Probabilistic AutoRegressive Neural Networks for Accurate Long-range ForecastingCode0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series PredictionCode0
Autoregressive Convolutional Neural Networks for Asynchronous Time SeriesCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Benchmarking time series classification -- Functional data vs machine learning approachesCode0
Improving Accuracy and Explainability of Online Handwriting RecognitionCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Autonomous Deep Quality Monitoring in Streaming EnvironmentsCode0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast TasksCode0
Learning Representations from EEG with Deep Recurrent-Convolutional Neural NetworksCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Adversarial Generation of Time-Frequency Features with application in audio synthesisCode0
Automatic Segmentation of the Placenta in BOLD MRI Time SeriesCode0
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical FeaturesCode0
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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