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 3651–3700 of 6748 papers

TitleStatusHype
Synthesising a Database of Parameterised Linear and Non-Linear Invariants for Time-Series Constraints—0
Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks—0
Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network—0
Synthetic Control Methods and Big Data—0
Synthetic Event Time Series Health Data Generation—0
Synthetic Photovoltaic and Wind Power Forecasting Data—0
Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks—0
System Identification in Multi-Actuator Hard Disk Drives with Colored Noises using Observer/Kalman Filter Identification (OKID) Framework—0
System identification using Bayesian neural networks with nonparametric noise models—0
T4PdM: a Deep Neural Network based on the Transformer Architecture for Fault Diagnosis of Rotating Machinery—0
Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion—0
Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages—0
Takens-inspired neuromorphic processor: a downsizing tool for random recurrent neural networks via feature extraction—0
Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification—0
Taming the Long Tail of Deep Probabilistic Forecasting—0
Tampered VAE for Improved Satellite Image Time Series Classification—0
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting—0
Targeted Attacks on Timeseries Forecasting—0
Task-aware Similarity Learning for Event-triggered Time Series—0
Task-Oriented Prediction and Communication Co-Design for Haptic Communications—0
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach—0
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy—0
Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400—0
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference—0
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information—0
Teaching DNNs to design fast fashion—0
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example—0
Technical Progress Analysis Using a Dynamic Topic Model for Technical Terms to Revise Patent Classification Codes—0
Techniques for clustering interaction data as a collection of graphs—0
Techniques for multifractal spectrum estimation in financial time series—0
Techniques for visualizing LSTMs applied to electrocardiograms—0
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data—0
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services—0
Telling cause from effect in deterministic linear dynamical systems—0
Tempered Stable Processes with Time Varying Exponential Tails—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Temporal Autoencoding Improves Generative Models of Time Series—0
Temporal-Clustering Invariance in Irregular Healthcare Time Series—0
Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices—0
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting—0
Temporal Disaggregation of the Cumulative Grass Growth—0
Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets—0
Temporal Feature Selection on Networked Time Series—0
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration—0
Temporal Graph Convolutional Networks for Automatic Seizure Detection—0
Temporal Graph Signal Decomposition—0
OptStream: Releasing Time Series Privately—0
Temporal-lobe Epilepsy: Harmonic and Anharmonic Periodicity in Microeletrode Voltage—0
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data—0
Temporally Folded Convolutional Neural Networks for Sequence Forecasting—0
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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