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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3275 of 6748 papers

TitleStatusHype
Data-Driven Copy-Paste Imputation for Energy Time SeriesCode0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks—0
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph—0
Parkinson's Disease Diagnosis Using Deep Learning—0
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing—0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19—0
Leveraging latent persistency in United States patent and trademark applications to gain insight into the evolution of an innovation-driven economy—0
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural NetworksCode1
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
The 2020 Global Stock Market Crash: Endogenous or Exogenous?—0
Detecting residues of cosmic events using residual neural network—0
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data—0
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code—0
Graph Edit NetworksCode0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
MODALS: Modality-agnostic Automated Data Augmentation in the Latent SpaceCode1
CLARE-GAN: GENERATION OF CLASS-SPECIFIC TIME SERIES—0
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems—0
Anomaly detection and regime searching in fitness-tracker data—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Time Series Counterfactual Inference with Hidden Confounders—0
Latent Convergent Cross Mapping—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