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 2976–3000 of 6748 papers

TitleStatusHype
Learning Discriminative Prototypes with Dynamic Time WarpingCode1
An Efficient Method for the Classification of Croplands in Scarce-Label RegionsCode0
Temporal Cluster Matching for Change Detection of Structures from Satellite ImageryCode1
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—0
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transportCode0
Deep Time Series Models for Scarce Data—0
Interpretable Feature Construction for Time Series Extrinsic Regression—0
Tomography of time-dependent quantum spin networks with machine learning—0
Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning—0
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
A machine learning approach to itinerary-level booking prediction in competitive airline markets—0
Online Learning with Radial Basis Function Networks—0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
A novel weighted approach for time series forecasting based on visibility graph—0
Anticipating synchronization with machine learning—0
Spectral Temporal Graph Neural Network for Multivariate Time-series ForecastingCode1
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning—0
Visualising Deep Network's Time-Series Representations—0
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events—0
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation—0
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Machine Learning Prediction of Time-Varying Rayleigh Channels—0
Extension of the Lagrange multiplier test for error cross-section independence to large panels with non normal errors—0
Streaming Linear System Identification with Reverse Experience Replay—0
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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