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 2376–2400 of 6748 papers

TitleStatusHype
A new look at the anthropogenic global warming consensus: an econometric forecast based on the ARIMA model of paleoclimate series—0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks—0
Signal Classification using Smooth Coefficients of Multiple wavelets—0
Early and Revocable Time Series Classification—0
Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google TrendsCode1
Neural forecasting at scale—0
Merlion: A Machine Learning Library for Time SeriesCode1
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Modeling Regime Shifts in Multiple Time Series—0
Learning to Forecast Dynamical Systems from Streaming DataCode0
Topology, Convergence, and Reconstruction of Predictive States—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
Hydroelectric Generation Forecasting with Long Short Term Memory (LSTM) Based Deep Learning Model for Turkey—0
Change of human mobility during COVID-19: A United States case studyCode0
A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines—0
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba—0
Improvement of Flood Extent Representation with Remote Sensing Data and Data Assimilation—0
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast ModelsCode1
Machine learning methods for modelling and analysis of time series signals in geoinformatics—0
Trading styles and long-run variance of asset prices—0
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
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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