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 1626–1650 of 6748 papers

TitleStatusHype
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States—0
Economic state classification and portfolio optimisation with application to stagflationary environments—0
A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction—0
Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full VersionCode1
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time SeriesCode0
Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan RegionCode0
A Cellular Automaton Model for the generation of Brainwaves—0
Enhancing Transformer Efficiency for Multivariate Time Series Classification—0
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional NetworksCode0
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach—0
Implications of Mortality Displacement for Effect Modification and Selection Bias—0
Using Multiple Instance Learning for Explainable Solar Flare PredictionCode0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
HYDRA: Competing convolutional kernels for fast and accurate time series classificationCode1
Domino: Discovering Systematic Errors with Cross-Modal EmbeddingsCode2
Rough volatility: fact or artefact?—0
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference—0
A platform for causal knowledge representation and inference in industrial fault diagnosis based on cubic DUCG—0
Wind speed forecast using random forest learning method—0
A Deep Learning Approach to Probabilistic Forecasting of WeatherCode0
Muscle Vision: Real Time Keypoint Based Pose Classification of Physical Exercises—0
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change SegmentationCode0
Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical InvestigationCode0
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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