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

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Showing 6176–6200 of 6748 papers

TitleStatusHype
Derivative Delay Embedding: Online Modeling of Streaming Time SeriesCode0
Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences—0
Drifting states and synchronization induced chaos in autonomous networks of excitable neurons—0
Privacy-Friendly Mobility Analytics using Aggregate Location Data—0
On the adoption of abductive reasoning for time series interpretationCode1
Predicting Future Shanghai Stock Market Price using ANN in the Period 21-Sep-2016 to 11-Oct-2016—0
Scaling up Echo-State Networks with multiple light scattering—0
Learning conditional independence structure for high-dimensional uncorrelated vector processes—0
Multiplex visibility graphs to investigate recurrent neural networks dynamics—0
Identifying Topology of Power Distribution Networks Based on Smart Meter Data—0
Options as Silver Bullets: Valuation of Term Loans, Inventory Management, Emissions Trading and Insurance Risk Mitigation using Option Theory—0
Automatic Generation of Student Report Cards—0
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks—0
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship—0
Foreign Exchange Market Performance: Evidence from Bivariate Time Series Approach—0
Learning Temporal Dependence from Time-Series Data with Latent Variables—0
State Duration and Interval Modeling in Hidden Semi-Markov Model for Sequential Data Analysis—0
"Butterfly Effect" vs Chaos in Energy Futures Markets—0
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder—0
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves—0
Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Training Echo State Networks with Regularization through Dimensionality Reduction—0
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC—0
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM—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