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 5676–5700 of 6748 papers

TitleStatusHype
Emulating dynamic non-linear simulators using Gaussian processes—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems—0
On Lyapunov exponents and adversarial perturbation—0
Deep Echo State Networks for Diagnosis of Parkinson's Disease—0
Learning Representative Temporal Features for Action Recognition—0
A Generative Modeling Approach to Limited Channel ECG Classification—0
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data—0
Neural Granger CausalityCode0
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space—0
Mining Sub-Interval Relationships In Time Series Data—0
Admissible Time Series Motif Discovery with Missing Data—0
The Affine Wealth Model: An agent-based model of asset exchange that allows for negative-wealth agents and its empirical validationCode0
D2KE: From Distance to Kernel and Embedding—0
Graph2Seq: Scalable Learning Dynamics for Graphs—0
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical CareCode0
Predicting crypto-currencies using sparse non-Gaussian state space models—0
Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series—0
Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS—0
Latent Variable Time-varying Network InferenceCode0
Inferring the time-varying functional connectivity of large-scale computer networks from emitted events—0
Differentiable Dynamic Programming for Structured Prediction and Attention—0
Learning Correlation Space for Time Series—0
The Power of Linear Recurrent Neural NetworksCode0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
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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