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 2151–2175 of 6748 papers

TitleStatusHype
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Alpha Discovery Neural Network based on Prior Knowledge—0
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning—0
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series—0
Approximation Theory of Convolutional Architectures for Time Series Modelling—0
5G Traffic Prediction with Time Series Analysis—0
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Approximation algorithms for confidence bands for time series—0
CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation—0
A Statistical Recurrent Stochastic Volatility Model for Stock Markets—0
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
Structural clustering of volatility regimes for dynamic trading strategies—0
Approximating DTW with a convolutional neural network on EEG data—0
Empowering Time Series Analysis with Large Language Models: A Survey—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Clustering Time-Series Energy Data from Smart Meters—0
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction—0
Clustering Time Series Data through Autoencoder-based Deep Learning Models—0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Approximate Newton-based statistical inference using only stochastic gradients—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Emulating dynamic non-linear simulators using Gaussian processes—0
Clustering Time Series and the Surprising Robustness of HMMs—0
Clustering piecewise stationary processes—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