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 3076–3100 of 6748 papers

TitleStatusHype
Arm order recognition in multi-armed bandit problem with laser chaos time series—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
High-dimensional Time Series Prediction with Missing Values—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs—0
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach—0
Free congruence: an exploration of expanded similarity measures for time series data—0
Computer activity learning from system call time series—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator—0
High-recall causal discovery for autocorrelated time series with latent confounders—0
Fractional trends and cycles in macroeconomic time series—0
High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
HiPPO-KAN: Efficient KAN Model for Time Series Analysis—0
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory—0
Fractional integration and cointegration—0
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting—0
Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting—0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
A metric to compare the anatomy variation between image time series—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