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 2001–2025 of 6748 papers

TitleStatusHype
Sentiment Analysis of Homeric Text: The 1st Book of Iliad—0
SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks—0
Data Imputation for Multivariate Time Series Sensor Data with Large Gaps of Missing DataCode0
Visualizing Parliamentary Speeches as Networks: the DYLEN Tool—0
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction—0
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting—0
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series—0
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsCode0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasksCode0
CHALLENGER: Training with Attribution Maps—0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
Temporal Multiresolution Graph Neural Networks For Epidemic PredictionCode0
Finding Patterns in Visualized Data by Adding Redundant Visual Information—0
Deep Generators on Commodity Markets; application to Deep Hedging—0
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—0
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Topological Hidden Markov Models—0
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time SeriesCode0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Conformal Prediction Intervals with Temporal DependenceCode0
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—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