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 551–600 of 6748 papers

TitleStatusHype
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative ModelsCode1
pyWATTS: Python Workflow Automation Tool for Time SeriesCode1
On Contrastive Representations of Stochastic ProcessesCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
SCINet: Time Series Modeling and Forecasting with Sample Convolution and InteractionCode1
Voice2Series: Reprogramming Acoustic Models for Time Series ClassificationCode1
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian FilteringCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Next Generation Reservoir ComputingCode1
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
WAX-ML: A Python library for machine learning and feedback loops on streaming dataCode1
Semi-supervised Time Series Classification by Temporal Relation PredictionCode1
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire DetectionCode1
RNN with Particle Flow for Probabilistic Spatio-temporal ForecastingCode1
Neighborhood Contrastive Learning Applied to Online Patient MonitoringCode1
Explaining Time Series Predictions with Dynamic MasksCode1
Manifold Topology Divergence: a Framework for Comparing Data ManifoldsCode1
Parameter Inference with Bifurcation DiagramsCode1
Deep Learning Statistical ArbitrageCode1
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic ForecastingCode1
Deep Switching State Space Model (DS^3M) for Nonlinear Time Series Forecasting with Regime SwitchingCode1
Price graphs: Utilizing the structural information of financial time series for stock predictionCode1
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic RegressionCode1
Unsupervised Representation Learning for Time Series with Temporal Neighborhood CodingCode1
Fast, Accurate and Interpretable Time Series Classification Through RandomizationCode1
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint DetectionCode1
Arbitrage-free neural-SDE market modelsCode1
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.Code1
Quantified Sleep: Machine learning techniques for observational n-of-1 studiesCode1
Monash Time Series Forecasting ArchiveCode1
Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric ClassificationCode1
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series ForecastingCode1
SIRNN: A Math Library for Secure RNN InferenceCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
Using Twitter Attribute Information to Predict Stock PricesCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Learning future terrorist targets through temporal meta-graphsCode1
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series DataCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
HIVE-COTE 2.0: a new meta ensemble for time series classificationCode1
Adversarial Sticker: A Stealthy Attack Method in the Physical WorldCode1
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxCode1
Weak Form Generalized Hamiltonian LearningCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
DeepSITH: Efficient Learning via Decomposition of What and When Across Time ScalesCode1
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
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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