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 2251–2300 of 6748 papers

TitleStatusHype
Joint Normality Test Via Two-Dimensional Projection—0
Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop MappingCode0
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia—0
Predictive Maintenance for General Aviation Using Convolutional Transformers—0
Time Series Forecasting Using Manifold Learning—0
PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
5G Traffic Prediction with Time Series Analysis—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
Automated Testing of AI Models—0
An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter—0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
Overcoming limited battery data challenges: A coupled neural network approach—0
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network—0
Bayesian autoregressive spectral estimation—0
Attention Augmented Convolutional Transformer for Tabular Time-series—0
RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning—0
Interpreting intermediate convolutional layers in unsupervised acoustic word classification—0
Truth-Conditional Captioning of Time Series DataCode1
Cross-Modal Virtual Sensing for Combustion Instability Monitoring—0
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization—0
Causality and Generalizability: Identifiability and Learning Methods—0
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting—0
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time SeriesCode1
Uncertainty, volatility and the persistence norms of financial time series—0
Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series—0
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series—0
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values—0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
Multi Scale Graph Wavenet for Wind Speed Forecasting—0
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field—0
Long-term Prediction of Nonlinear Time Series Using Autoencoder and Echo State Networks—0
An Optimally Weighted Echo State Neural Network for Highly Chaotic Time Series Modelling—0
TransTCN: An Attention-based TCN Framework for Sequential Modeling—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Less is more: Selecting the right benchmarking set of data for time series classification—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
Tractable Dendritic RNNs for Identifying Unknown Nonlinear Dynamical Systems—0
Multi-Task Processes—0
Objective Evaluation of Deep Visual Interpretations on Time Series Data—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series—0
A Study of Aggregation of Long Time-series Input for LSTM Neural Networks—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