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

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Showing 676–700 of 6748 papers

TitleStatusHype
Design-time Fashion Popularity Forecasting in VR Environments—0
Temporal Weights—0
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series—0
Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
On Mini-Batch Training with Varying Length Time SeriesCode0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Nonparametric Independent Component Analysis for the Sources with Mixed Spectra—0
Land use/land cover dynamics on vulnerable regions in Uruguay approached by a method combining Maximum Entropy and Population Dynamics—0
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management—0
Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories—0
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Multi-Dimensional Self Attention based Approach for Remaining Useful Life EstimationCode1
Neural Continuous-Time Markov Models—0
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period—0
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach—0
Towards Better Long-range Time Series Forecasting using Generative Forecasting—0
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series—0
Unsupervised Flood Detection on SAR Time Series—0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Time series numerical association rule mining variants in smart agriculture—0
Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines—0
Sequential Predictive Conformal Inference for Time SeriesCode1
Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkCode1
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time SeriesCode0
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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