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 3001–3025 of 6748 papers

TitleStatusHype
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations—0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints—0
Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime—0
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation—0
Handling Missing Observations with an RNN-based Prediction-Update Cycle—0
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review—0
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks—0
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes—0
Convolutional Sequence Modeling Revisited—0
Causal impact of severe events on electricity demand: The case of COVID-19 in Japan—0
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks—0
End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker—0
Causal Hidden Markov Model for Time Series Disease Forecasting—0
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing—0
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series—0
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—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