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 5551–5600 of 6748 papers

TitleStatusHype
Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality—0
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics—0
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series—0
Random matrix approach to estimation of high-dimensional factor models—0
Random pattern and frequency generation using a photonic reservoir computer with output feedback—0
Random Projection Filter Bank for Time Series Data—0
Random selection of factors preserves the correlation structure in a linear factor model to a high degree—0
Random Similarity Forests—0
Random thoughts about Complexity, Data and Models—0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—0
Ranking and significance of variable-length similarity-based time series motifs—0
Ranking election issues through the lens of social media—0
Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics—0
RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning—0
RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product—0
RAPID: Early Classification of Explosive Transients using Deep Learning—0
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown—0
Rapid Time Series Prediction with a Hardware-Based Reservoir Computer—0
Rate-Agnostic (Causal) Structure Learning—0
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data—0
Reactmine: a statistical search algorithm for inferring chemical reactions from time series data—0
Reading Documents for Bayesian Online Change Point Detection—0
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art—0
Real-time Change Point Detection using On-line Topic Models—0
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs—0
Real time clustering of time series using triangular potentials—0
Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data—0
Real-time Drift Detection on Time-series Data—0
Real time error detection in metal arc welding process using Artificial Neural Netwroks—0
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras—0
Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs—0
Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet—0
Real-time NLOS/LOS Identification for Smartphone-based Indoor Positioning System using WiFi RTT and RSS—0
Real-time Prediction of Bitcoin Bubble Crashes—0
Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis—0
Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis—0
Reasoning for Improved Sensor Data Interpretation in a Smart Home—0
Recent Advances in Recurrent Neural Networks—0
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery—0
Recent scaling properties of Bitcoin price returns—0
Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts—0
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack—0
Recognizing Ornaments in Vocal Indian Art Music with Active Annotation—0
Recognizing Temporal Linguistic Expression Pattern of Individual with Suicide Risk on Social Media—0
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection—0
Reconstructing a dynamical system and forecasting time series by self-consistent deep learning—0
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations—0
Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops—0
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior—0
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates—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