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 5426–5450 of 6748 papers

TitleStatusHype
Study of Set-Membership Adaptive Kernel Algorithms—0
Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency DomainsCode1
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values—0
Data augmentation using synthetic data for time series classification with deep residual networksCode0
OptStream: Releasing Time Series Privately—0
Predicting Learning Status in MOOCs using LSTM—0
Real-time Change Point Detection using On-line Topic Models—0
Mod-DeepESN: Modular Deep Echo State Network—0
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks—0
Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model—0
Modeling joint probability distribution of yield curve parameters—0
Kernel Density Estimation-Based Markov Models with Hidden State—0
On the use of Singular Spectrum Analysis—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—0
Discovering physical concepts with neural networksCode0
Entropy Analysis of Financial Time Series—0
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study—0
A Capsule Network for Traffic Speed Prediction in Complex Road NetworksCode0
Deep Learning for Epidemiological Predictions—0
Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors—0
Quantifying Volatility Reduction in German Day-ahead Spot Market in the Period 2006 through 2016—0
Approximate Collapsed Gibbs Clustering with Expectation Propagation—0
Rapid Time Series Prediction with a Hardware-Based Reservoir Computer—0
Clustering Macroeconomic Time Series—0
To Post or Not to Post: Using Online Trends to Predict Popularity of Offline Content—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