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 5826–5850 of 6748 papers

TitleStatusHype
BrazilDAM: A Benchmark dataset for Tailings Dam DetectionCode0
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time SeriesCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurvesCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian ProcessesCode0
Recursive Tree Grammar AutoencodersCode0
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time SeriesCode0
The effects of regularisation on RNN models for time series forecasting: Covid-19 as an exampleCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
Highly Scalable and Provably Accurate Classification in Poincare BallsCode0
Boosting: Why You Can Use the HP FilterCode0
Reduced Order Probabilistic Emulation for Physics-Based Thermosphere ModelsCode0
Block-Structure Based Time-Series Models For Graph SequencesCode0
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingCode0
Nonparametric Value-at-Risk via Sieve EstimationCode0
A Deep Learning Approach to Probabilistic Forecasting of WeatherCode0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time SeriesCode0
Reforming the State-Based Forward Guidance through Wage Growth Rate Threshold: Evidence from FRB/US SimulationsCode0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
High-dimensional regression with potential prior information on variable importanceCode0
High dimensional regression for regenerative time-series: an application to road traffic modelingCode0
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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