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 4451–4475 of 6748 papers

TitleStatusHype
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms—0
Reinforcement Learning with Convolutional Reservoir Computing—0
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links—0
Warped Input Gaussian Processes for Time Series ForecastingCode0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data—0
Regression with Uncertainty Quantification in Large Scale Complex Data—0
Work-in-progress: a deep learning strategy for I/O scheduling in storage systemsCode0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints—0
Differential Bayesian Neural Nets—0
Capacity of the covariance perceptron—0
Time-series Generative Adversarial Networks—0
Machine learning applications in time series hierarchical forecasting—0
Latent Ordinary Differential Equations for Irregularly-Sampled Time SeriesCode0
ODE2VAE: Deep generative second order ODEs with Bayesian neural networksCode0
Time-series Generative Adversarial Networks—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Learning Representations for Time Series ClusteringCode0
Shallow RNN: Accurate Time-series Classification on Resource Constrained DevicesCode0
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksCode1
Perceiving the arrow of time in autoregressive motion—0
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019—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