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 5651–5700 of 6748 papers

TitleStatusHype
Emulating dynamic non-linear simulators using Gaussian processes—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
Mining Sub-Interval Relationships In Time Series Data—0
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space—0
The Affine Wealth Model: An agent-based model of asset exchange that allows for negative-wealth agents and its empirical validationCode0
Admissible Time Series Motif Discovery with Missing Data—0
Graph2Seq: Scalable Learning Dynamics for Graphs—0
D2KE: From Distance to Kernel and Embedding—0
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical CareCode0
Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series—0
Predicting crypto-currencies using sparse non-Gaussian state space models—0
Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS—0
Inferring the time-varying functional connectivity of large-scale computer networks from emitted events—0
Latent Variable Time-varying Network InferenceCode0
Differentiable Dynamic Programming for Structured Prediction and Attention—0
Learning Correlation Space for Time Series—0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
The Power of Linear Recurrent Neural NetworksCode0
TSViz: Demystification of Deep Learning Models for Time-Series AnalysisCode0
Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation—0
Lie Transform--based Neural Networks for Dynamics Simulation and Learning—0
Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis—0
Weakly-supervised Dictionary Learning—0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
Probabilistic Recurrent State-Space ModelsCode1
ReNN: Rule-embedded Neural Networks—0
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG IdentificationCode0
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding—0
Nonlinear Dimensionality Reduction on Graphs—0
Spurious seasonality detection: a non-parametric test proposal—0
Empirical observations of ultraslow diffusion driven by the fractional dynamics in languages: Dynamical statistical properties of word counts of already popular words—0
Non-parametric Sparse Additive Auto-regressive Network Models—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Characterization of catastrophic instabilities: Market crashes as paradigm—0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs—0
Fractal analyses of networks of integrate-and-fire stochastic spiking neurons—0
Large-Scale Simulation of Multi-Asset Ising Financial Markets—0
A First Option Calibration of the GARCH Diffusion Model by a PDE Method—0
Invariants of multidimensional time series based on their iterated-integral signature—0
Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events—0
Deep Canonically Correlated LSTMs—0
Time Series Segmentation through Automatic Feature Learning—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