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 6676–6700 of 6748 papers

TitleStatusHype
Fractal structures in Adversarial Prediction—0
Unsupervised model-free representation learning—0
Learning Heteroscedastic Models by Convex Programming under Group Sparsity—0
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures—0
Towards The Inductive Acquisition of Temporal Knowledge—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
An Introductory Study on Time Series Modeling and Forecasting—0
Structure Discovery in Nonparametric Regression through Compositional Kernel SearchCode0
A Latent Source Model for Nonparametric Time Series Classification—0
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory—0
Macro-Economic Time Series Modeling and Interaction Networks—0
Nonparametric risk bounds for time-series forecasting—0
Modelling Reciprocating Relationships with Hawkes Processes—0
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task—0
Locating Changes in Highly Dependent Data with Unknown Number of Change Points—0
Expectation Propagation in Gaussian Process Dynamical Systems—0
Identification of Recurrent Patterns in the Activation of Brain Networks—0
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data—0
Optimally fuzzy temporal memory—0
Cross-Lingual Topic Alignment in Time Series Japanese / Chinese News—0
Reducing statistical time-series problems to binary classification—0
Locally adaptive factor processes for multivariate time series—0
Online Learning with Predictable Sequences—0
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version—0
Identifying Constant and Unique Relations by using Time-Series Text—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