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 4901–4925 of 6748 papers

TitleStatusHype
Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—0
Kernel Hypothesis Testing with Set-valued Data—0
Routine Modeling with Time Series Metric Learning—0
Attending to Emotional NarrativesCode0
Fast ES-RNN: A GPU Implementation of the ES-RNN AlgorithmCode0
Takens-inspired neuromorphic processor: a downsizing tool for random recurrent neural networks via feature extraction—0
A Quantum Field Theory of Representation Learning—0
Action Prediction in Humans and Robots—0
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly DetectionCode0
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model—0
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting—0
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksCode0
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization—0
Maximum Entropy approach to multivariate time series randomization—0
Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates—0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
A multifactorial evaluation framework for gene regulatory network reconstruction—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
A 1d convolutional network for leaf and time series classificationCode0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
Teaching DNNs to design fast fashion—0
Clustering piecewise stationary processes—0
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series DataCode0
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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