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

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Showing 2151–2200 of 6748 papers

TitleStatusHype
Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models—0
Sig-Wasserstein GANs for Time Series GenerationCode1
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systemsCode1
Brain dynamics via Cumulative Auto-Regressive Self-Attention—0
Nested Multiple Instance Learning with Attention MechanismsCode0
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE—0
ECG synthesis with Neural ODE and GAN models—0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Robust and efficient change point detection using novel multivariate rank-energy GoF test—0
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index—0
Improved FRQI on superconducting processors and its restrictions in the NISQ era—0
Aligned Multi-Task Gaussian Process—0
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
Multi-Task Neural ProcessesCode0
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic TomographyCode0
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction ModelsCode0
Coresets for Time Series Clustering—0
Warped Dynamic Linear Models for Time Series of CountsCode0
Validation Methods for Energy Time Series Scenarios from Deep Generative Models—0
GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series—0
Forecasting with a Panel Tobit Model—0
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data—0
Testing and Estimating Structural Breaks in Time Series and Panel Data in StataCode1
ClaSP - Time Series SegmentationCode1
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Cluster-and-Conquer: A Framework For Time-Series Forecasting—0
PARIS: Personalized Activity Recommendation for Improving Sleep Quality—0
Deep Explicit Duration Switching Models for Time SeriesCode1
Data-Driven Time Series Reconstruction for Modern Power Systems Research—0
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Neural Flows: Efficient Alternative to Neural ODEsCode1
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications—0
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures—0
On Learning Prediction-Focused Mixtures—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
A Pipeline for Graph-Based Monitoring of the Changes in the Information Space of Russian Social Media during the Lockdown—0
Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field—0
Path Signature Area-Based Causal Discovery in Coupled Time SeriesCode0
Clustering Market Regimes using the Wasserstein DistanceCode0
Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops—0
An Empirical Evaluation of Time-Series Feature SetsCode1
High-resolution rainfall-runoff modeling using graph neural network—0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Stock exchange shares ranking and binary-ternary compressive coding—0
Adversarial attacks against Bayesian forecasting dynamic models—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