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

Showing 18761900 of 6748 papers

TitleStatusHype
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural NetworkCode0
Boosting: Why You Can Use the HP FilterCode0
Anomaly detection in dynamic networksCode0
Automatic alignment of surgical videos using kinematic dataCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time SeriesCode0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
Distributed and parallel time series feature extraction for industrial big data applicationsCode0
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive LearningCode0
DeepTFP: Mobile Time Series Data Analytics based Traffic Flow PredictionCode0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Distributional conformal predictionCode0
Dropout Feature Ranking for Deep Learning ModelsCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
A Capsule Network for Traffic Speed Prediction in Complex Road NetworksCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Time Series Data Cleaning: From Anomaly Detection to Anomaly RepairingCode0
Block-Structure Based Time-Series Models For Graph SequencesCode0
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingCode0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality AssessmentCode0
Discrete signature and its application to financeCode0
Discovering physical concepts with neural networksCode0
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge DistillationCode0
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time SeriesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified