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 57515800 of 6748 papers

TitleStatusHype
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
Action Recognition Using Volumetric Motion RepresentationsCode0
Data Consistency Approach to Model ValidationCode0
The DeepCAR Method: Forecasting Time-Series Data That Have Change PointsCode0
DSANet: Dual Self-Attention Network for Multivariate Time Series ForecastingCode0
Dropout Feature Ranking for Deep Learning ModelsCode0
Neural Pharmacodynamic State Space ModelingCode0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
Inferring network connectivity from event timing patternsCode0
Inferring species interactions using Granger causality and convergent cross mappingCode0
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural NetworkCode0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metricCode0
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Neural State-Space Modeling with Latent Causal-Effect DisentanglementCode0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time SeriesCode0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
Domain Adaptation with Representation Learning and Nonlinear Relation for Time SeriesCode0
NeuralWarp: Time-Series Similarity with Warping NetworksCode0
Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor StreamsCode0
Neuronal architecture extracts statistical temporal patternsCode0
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one MapsCode0
Data augmentation using synthetic data for time series classification with deep residual networksCode0
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GANCode0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge SolutionCode0
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series PredictionCode0
NEWMA: a new method for scalable model-free online change-point detectionCode0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
Time Series Using Exponential Smoothing CellsCode0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
BRITS: Bidirectional Recurrent Imputation for Time SeriesCode0
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time SeriesCode0
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural NetworksCode0
A Better Alternative to Piecewise Linear Time Series SegmentationCode0
Spatiotemporal Attention for Multivariate Time Series Prediction and InterpretationCode0
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time seriesCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving NoiseCode0
Spatiotemporal Attention Networks for Wind Power ForecastingCode0
Distributional conformal predictionCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Classification with 2-D Convolutional Neural Networks for breast cancer diagnosisCode0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal 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