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 6301–6350 of 6748 papers

TitleStatusHype
Persistent Homology of Attractors For Action Recognition—0
Short-term time series prediction using Hilbert space embeddings of autoregressive processes—0
Modeling Time Series Similarity with Siamese Recurrent Networks—0
Evaluation and Ensembling of Methods for Reverse Engineering of Brain Connectivity from Imaging Data—0
Learning Network of Multivariate Hawkes Processes: A Time Series Approach—0
Clustering Financial Time Series: How Long is Enough?—0
Real time error detection in metal arc welding process using Artificial Neural Netwroks—0
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies—0
Support Vector Machines with Time Series Distance Kernels for Action ClassificationCode0
Coordination Event Detection and Initiator Identification in Time Series DataCode0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—0
Clustering Based Feature Learning on Variable StarsCode0
A Langevin model for complex cardiological time series—0
Modified Profile Likelihood Inference and Interval Forecast of the Burst of Financial Bubbles—0
Seeking for a fingerprint: analysis of point processes in actigraphy recording—0
Causal Discovery from Subsampled Time Series Data by Constraint Optimization—0
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series—0
Parsimonious modeling with Information Filtering Networks—0
Lagged and instantaneous dynamical influences related to brain structural connectivity—0
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems—0
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM—0
Solar energy production: Short-term forecasting and risk management—0
Scaling up Dynamic Topic ModelsCode0
Statistical modeling of isoform splicing dynamics from RNA-seq time series data—0
Learning Over Long Time Lags—0
Machine olfaction using time scattering of sensor multiresolution graphs—0
Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and β-Mixing—0
Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes—0
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version—0
Graph-based Predictable Feature Analysis—0
Kernels for sequentially ordered data—0
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference—0
Investigating echo state networks dynamics by means of recurrence analysis—0
Stochastic density effects on adult fish survival and implications for population fluctuations—0
Orthogonal Echo State Networks and stochastic evaluations of likelihoods—0
Model-Coupled Autoencoder for Time Series Visualisation—0
Bayesian inference of natural selection from allele frequency time series—0
Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge—0
A comparison among some Hurst exponent approaches to predict nascent bubbles in 500 company stocks—0
Improved graph-based SFA: Information preservation complements the slowness principle—0
Do Mature Economies Grow Exponentially?—0
Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset—0
Bursty and persistent properties of large-scale brain networks revealed with a point-based method for dynamic functional connectivity—0
Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector—0
On Clustering Time Series Using Euclidean Distance and Pearson Correlation—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Utilizing Temporal Information for Taxonomy Construction—0
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency—0
Statistical and Computational Guarantees for the Baum-Welch Algorithm—0
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction—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