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 1201–1225 of 6748 papers

TitleStatusHype
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity RecognitionCode0
Gated Res2Net for Multivariate Time Series AnalysisCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence FunctionsCode0
A signature-based machine learning model for bipolar disorder and borderline personality disorderCode0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
Sequence Prediction using Spectral RNNsCode0
Fully Neural Network based Model for General Temporal Point ProcessesCode0
A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 caseCode0
Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield SpreadCode0
Forecasting Time Series With Complex Seasonal Patterns Using Exponential SmoothingCode0
A Multi-Horizon Quantile Recurrent ForecasterCode0
Forecasting new diseases in low-data settings using transfer learningCode0
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian ApproachCode0
Forecasting Precipitable Water Vapor Using LSTMsCode0
Forecasting with Multiple SeasonalityCode0
FNetAR: Mixing Tokens with Autoregressive Fourier TransformsCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learningCode0
Coordination Event Detection and Initiator Identification in Time Series DataCode0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
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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