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
A Tale of Tail Covariances (and Diversified Tails)—0
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping—0
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You ThinkCode0
Deep Imbalanced Time-series Forecasting via Local Discrepancy DensityCode0
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting—0
Robust language-based mental health assessments in time and space through social media—0
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance—0
Chaotic Variational Auto encoder-based Adversarial Machine Learning—0
Detecting Rough Volatility: A Filtering Approach—0
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease ProgressionCode0
Statistical Inference with Stochastic Gradient Methods under φ-mixing Data—0
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers—0
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space—0
A metric to compare the anatomy variation between image time series—0
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference—0
Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting—0
The DeepCAR Method: Forecasting Time-Series Data That Have Change PointsCode0
Time-varying Signals Recovery via Graph Neural Networks—0
Information Theory Inspired Pattern Analysis for Time-series Data—0
Measuring city-scale green infrastructure drawdown dynamics using internet-connected sensors in Detroit—0
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts—0
Task-Oriented Prediction and Communication Co-Design for Haptic Communications—0
FedST: Secure Federated Shapelet Transformation for Time Series Classification—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