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 1226–1250 of 6748 papers

TitleStatusHype
Forecasting with Multiple SeasonalityCode0
Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield SpreadCode0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Forecasting Time Series With Complex Seasonal Patterns Using Exponential SmoothingCode0
Forecasting Precipitable Water Vapor Using LSTMsCode0
A Model of the Fed's View on InflationCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
Forecasting new diseases in low-data settings using transfer learningCode0
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time WarpingCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
FNetAR: Mixing Tokens with Autoregressive Fourier TransformsCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
Flipped Classroom: Effective Teaching for Time Series ForecastingCode0
Multimodal Transformer for Unaligned Multimodal Language SequencesCode0
Coordination Event Detection and Initiator Identification in Time Series DataCode0
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Forecasting and Granger Modelling with Non-linear Dynamical DependenciesCode0
A Data Cube of Big Satellite Image Time-Series for Agriculture MonitoringCode0
A Review of the Long Horizon Forecasting Problem in Time Series AnalysisCode0
AdaRNN: Adaptive Learning and Forecasting of Time SeriesCode0
A Review of Network Inference Techniques for Neural Activation Time SeriesCode0
Fitting stochastic predator-prey models using both population density and kill rate dataCode0
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variablesCode0
fETSmcs: Feature-based ETS model component selectionCode0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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