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 2901–2950 of 6748 papers

TitleStatusHype
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxCode1
Weak Form Generalized Hamiltonian LearningCode1
Analysis of bank leverage via dynamical systems and deep neural networks—0
Boosted Embeddings for Time Series Forecasting—0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Deep Transformer Networks for Time Series Classification: The NPP Safety Case—0
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
DeepSITH: Efficient Learning via Decomposition of What and When Across Time ScalesCode1
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Neural Network for Weighted Signal Temporal Logic—0
Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models—0
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data—0
Min(d)ing the President: A text analytic approach to measuring tax news—0
Bootstrap Inference for Hawkes and General Point Processes—0
Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air—0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series—0
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic SystemsCode0
Analysis of bio-electro-chemical signals from passive sweat-based wearable electro-impedance spectroscopy (EIS) towards assessing blood glucose modulations—0
AST: Audio Spectrogram TransformerCode2
Model Compression for Dynamic Forecast CombinationCode0
COHORTNEY: Non-Parametric Clustering of Event Sequences—0
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
Monte Carlo Simulation of SDEs using GANsCode1
Stock price prediction using Generative Adversarial NetworksCode1
Learnable Dynamic Temporal Pooling for Time Series ClassificationCode0
The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories—0
A Comparative Analysis of Machine Learning and Grey Models—0
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey—0
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies—0
Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels—0
Quick Line Outage Identification in Urban Distribution Grids via Smart Meters—0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
Online Non-linear Topology Identification from Graph-connected Time Series—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Time Series Analysis and Modeling to Forecast: a Survey—0
VisioRed: A Visualisation Tool for Interpretable Predictive MaintenanceCode0
Channel-Based Attention for LCC Using Sentinel-2 Time Series—0
Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting—0
Neural Transformation Learning for Deep Anomaly Detection Beyond ImagesCode1
Prediction of Landfall Intensity, Location, and Time of a Tropical CycloneCode0
Causal Hidden Markov Model for Time Series Disease Forecasting—0
Inferring, comparing and exploring ecological networks from time-series data through R packages constructnet, disgraph and dynet—0
Stiff Neural Ordinary Differential EquationsCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting—0
A cloud-IoT platform for passive radio sensing: challenges and application case studies—0
Gated Transformer Networks for Multivariate Time Series ClassificationCode1
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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