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 3351–3375 of 6748 papers

TitleStatusHype
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
Forecasting high-frequency financial time series: an adaptive learning approach with the order book data—0
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion—0
Distributed Online Learning with Multiple Kernels—0
Adaptable Hamiltonian neural networks—0
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff—0
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services—0
Learning orbital dynamics of binary black hole systems from gravitational wave measurementsCode0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Deep Video Prediction for Time Series Forecasting—0
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions—0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
Similarity measure for sparse time course data based on Gaussian processesCode0
When is Early Classification of Time Series Meaningful?—0
The SpaceNet Multi-Temporal Urban Development Challenge—0
Model-Attentive Ensemble Learning for Sequence Modeling—0
Optimal Prediction Intervals for Macroeconomic Time Series Using Chaos and NSGA II—0
Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows—0
Neural Pharmacodynamic State Space ModelingCode0
Bridging factor and sparse models—0
Phase Space Reconstruction Network for Lane Intrusion Action Recognition—0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
nTreeClus: a Tree-based Sequence Encoder for Clustering Categorical SeriesCode0
Approximate Bayes factors for unit root testing—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