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 3726–3750 of 6748 papers

TitleStatusHype
Testing the martingale difference hypothesis in high dimension—0
Test Models for Statistical Inference: Two-Dimensional Reaction Systems Displaying Limit Cycle Bifurcations and Bistability—0
Text authorship identified using the dynamics of word co-occurrence networks—0
Text Classification: Neural Networks VS Machine Learning Models VS Pre-trained Models—0
Textual Data for Time Series Forecasting—0
The 2020 Global Stock Market Crash: Endogenous or Exogenous?—0
Adequacy of time-series reduction for renewable energy systems—0
The Altes Family of Log-Periodic Chirplets and the Hyperbolic Chirplet Transform—0
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks—0
The Automatic Statistician: A Relational Perspective—0
The Bayesian Context Trees State Space Model for time series modelling and forecasting—0
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting—0
The boosted HP filter is more general than you might think—0
The Canonical Interval Forest (CIF) Classifier for Time Series Classification—0
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems—0
The Computational Capacity of LRC, Memristive and Hybrid Reservoirs—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories—0
The Counterfactual-Shapley Value: Attributing Change in System Metrics—0
The Creation and Validation of Load Time Series for Synthetic Electric Power Systems—0
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator—0
The data synergy effects of time-series deep learning models in hydrology—0
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems—0
The DOPE Distance is SIC: A Stable, Informative, and Computable Metric on Time Series And Ordered Merge Trees—0
The dual frequency RV-coupling coefficient: a novel measure for quantifying cross-frequency information transactions in the brain—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