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 57015750 of 6748 papers

TitleStatusHype
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returnsCode0
Network Inference via the Time-Varying Graphical LassoCode0
Anomaly detection in the dynamics of web and social networksCode0
Edge computing on TPU for brain implant signal analysisCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Spikebench: An open benchmark for spike train time-series classificationCode0
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction ModelsCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Anomaly detection in dynamic networksCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Gradient Importance Learning for Incomplete ObservationsCode0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
imputeTS: Time Series Missing Value Imputation in RCode0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
Neural circuits for dynamics-based segmentation of time seriesCode0
Reconstructing shared dynamics with a deep neural networkCode0
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Data-Driven Copy-Paste Imputation for Energy Time SeriesCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
Reconstructing large networks with time-varying interactionsCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksCode0
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series DataCode0
Dynamic process fault prediction using canonical variable trend analysisCode0
RecovDB: accurate and efficient missing blocks recovery for large time seriesCode0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Imbedding Deep Neural NetworksCode0
Approximate Factor Models for Functional Time SeriesCode0
Time Series Source Separation using Dynamic Mode DecompositionCode0
Neural Granger CausalityCode0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change SegmentationCode0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
Recovering lost and absent information in temporal networksCode0
Quantum open system identification via global optimization: Optimally accurate Markovian models of open systems from time-series dataCode0
Tree Echo State Autoencoders with GrammarsCode0
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning ApproachCode0
Recovery of Future Data via Convolution Nuclear Norm MinimizationCode0
Weak error rates for option pricing under linear rough volatilityCode0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural NetworksCode0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy 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