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 1876–1900 of 6748 papers

TitleStatusHype
Task-aware Similarity Learning for Event-triggered Time Series—0
Multiscale Causal Structure Learning—0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
Outlier detection of vital sign trajectories from COVID-19 patientsCode0
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time SeriesCode0
StockBot: Using LSTMs to Predict Stock PricesCode0
Rethinking Attention Mechanism in Time Series Classification—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
Synthesis of Parametric Hybrid Automata from Time SeriesCode0
Improved Batching Strategy For Irregular Time-Series ODE—0
Wasserstein multivariate auto-regressive models for modeling distributional time seriesCode0
Markovian Gaussian Process Variational Autoencoders—0
Dateformer: Time-modeling Transformer for Longer-term Series ForecastingCode0
IMG-NILM: A Deep learning NILM approach using energy heatmaps—0
A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture—0
Towards Neural Numeric-To-Text Generation From Temporal Personal Health DataCode0
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction—0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical FeaturesCode0
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical AnalysisCode0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series—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