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 2726–2750 of 6748 papers

TitleStatusHype
Causal Digital Twin from Multi-channel IoT—0
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic RegressionCode1
MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled TrajectoriesCode0
Deep Signature Statistics for Likelihood-free Time-series Models—0
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes—0
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning—0
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural NetworksCode0
Quantifying the Effects of COVID-19 on Restaurant Reviews—0
Unsupervised Representation Learning for Time Series with Temporal Neighborhood CodingCode1
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments—0
Fast, Accurate and Interpretable Time Series Classification Through RandomizationCode1
Pattern Discovery in Time Series with Byte Pair Encoding—0
An improved LogNNet classifier for IoT application—0
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing—0
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data—0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline SummarizationCode0
Accelerating Neural ODEs Using Model Order Reduction—0
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy OptimizationCode0
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint DetectionCode1
Short-Term Stock Price-Trend Prediction Using Meta-Learning—0
Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State—0
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Show:102550
← PrevPage 110 of 270Next →
All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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