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 4051–4075 of 6748 papers

TitleStatusHype
TSInsight: A local-global attribution framework for interpretability in time-series data—0
TSML (Time Series Machine Learnng)—0
TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis—0
tsrobprep - an R package for robust preprocessing of time series data—0
TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante—0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features—0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis—0
Twitter's Agenda-Setting Role: A Study of Twitter Strategy for Political Diversion—0
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset—0
Two-phase flow regime prediction using LSTM based deep recurrent neural network—0
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data—0
Two-Stage Framework for Seasonal Time Series Forecasting—0
Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series—0
Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network—0
Ultra-Fast Shapelets for Time Series Classification—0
Ultraslow diffusion in language: Dynamics of appearance of already popular adjectives on Japanese blogs—0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition—0
Estimating and backtesting risk under heavy tails—0
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference—0
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift—0
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
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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