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

Showing 46514700 of 6748 papers

TitleStatusHype
Set Functions for Time SeriesCode0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison0
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS0
Adversarially learned anomaly detection for time series data0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS0
RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data0
Continuous Convolutional Neural Network forNonuniform Time Series0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning0
Recurrent Neural Networks are Universal Filters0
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction0
Temporal Probabilistic Asymmetric Multi-task Learning0
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization0
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series0
UNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS0
Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps0
Actor-Critic Approach for Temporal Predictive Clustering0
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers0
Explaining Time Series by Counterfactuals0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario0
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep NetworksCode0
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time SeriesCode0
Interpretable Models for Understanding Immersive Simulations0
Econometric modelling and forecasting of intraday electricity prices0
Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations0
Using Machine Learning to Predict Realized Variance0
PyIT2FLS: A New Python Toolkit for Interval Type 2 Fuzzy Logic SystemsCode0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution0
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks0
Time Series Modeling for Dream Team in Fantasy Premier LeagueCode0
Shape and Time Distortion Loss for Training Deep Time Series Forecasting ModelsCode1
Synthesis of Realistic ECG using Generative Adversarial NetworksCode1
Timage -- A Robust Time Series Classification PipelineCode0
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids0
Bifurcation Spiking Neural Network0
Distributional conformal predictionCode0
sktime: A Unified Interface for Machine Learning with Time Series0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning0
Towards a Rigorous Evaluation of XAI Methods on Time Series0
Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models0
Spatiotemporal Attention Networks for Wind Power ForecastingCode0
GENDIS: GENetic DIscovery of ShapeletsCode0
Interpolation-Prediction Networks for Irregularly Sampled Time SeriesCode0
Meta-Learning for Few-Shot Time Series Classification0
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing0
Activity recognition using ST-GCN with 3D motion data0
Using Clinical Notes with Time Series Data for ICU ManagementCode0
Reinforcement Learning for Portfolio ManagementCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified