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 5576–5600 of 6748 papers

TitleStatusHype
30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine—0
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections—0
Using Quantum Mechanics to Cluster Time Series—0
Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning—0
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs—0
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series—0
Robustness of sentence length measures in written texts—0
An Evaluation of Classification and Outlier Detection Algorithms—0
Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition—0
Scalable Visualisation of Sentiment and Stance—0
EuroGames16: Evaluating Change Detection in Online ConversationCode0
Towards Experienced Anomaly Detector through Reinforcement Learning—0
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis—0
Learning from multivariate discrete sequential data using a restricted Boltzmann machine model—0
Deep learning approach to Fourier ptychographic microscopyCode0
Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series—0
Adaptive pooling operators for weakly labeled sound event detectionCode0
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint—0
Deep Learning for Predicting Asset ReturnsCode0
On The Complexity of Sparse Label Propagation—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Block-Structure Based Time-Series Models For Graph SequencesCode0
Econometric Modeling of Regional Electricity Spot Prices in the Australian Market—0
Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery—0
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers—0
Show:102550
← PrevPage 224 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