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 1026–1050 of 6748 papers

TitleStatusHype
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification—0
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour—0
A walk through of time series analysis on quantum computers—0
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
A Visibility Graph Averaging Aggregation Operator—0
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting—0
A conditional likelihood is required to estimate the selection coefficient in ancient DNA—0
A Benchmark Study on Time Series Clustering—0
Characterization of electric consumers through an automated clustering pipeline—0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain—0
Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs—0
Chaos may enhance expressivity in cerebellar granular layer—0
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
A new measure between sets of probability distributions with applications to erratic financial behavior—0
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—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