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 401–425 of 6748 papers

TitleStatusHype
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
Edge computing on TPU for brain implant signal analysisCode0
Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms—0
Learning Hidden Markov Models Using Conditional Samples—0
Your time series is worth a binary image: machine vision assisted deep framework for time series forecastingCode0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You ThinkCode0
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative NetworkCode1
A Tale of Tail Covariances (and Diversified Tails)—0
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting—0
Deep Imbalanced Time-series Forecasting via Local Discrepancy DensityCode0
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping—0
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal ProfilesCode1
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
LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended VersionCode1
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Set Features for Fine-grained Anomaly DetectionCode1
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space—0
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers—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