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 4276–4300 of 6748 papers

TitleStatusHype
Forecasting in multivariate irregularly sampled time series with missing values—0
TSInsight: A local-global attribution framework for interpretability in time-series data—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack—0
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder—0
Spatio-Temporal Graph Structure Learning for Traffic Forecasting—0
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units—0
Enhance the performance of navigation: A two-stage machine learning approach—0
Time-varying volatility in Bitcoin market and information flow at minute-level frequency—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—0
Stationarity of Time-Series on Graph via Bivariate Translation Invariance—0
Machine Learning Algorithms for Financial Asset Price Forecasting—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
Adversarial Attacks on Multivariate Time Series—0
When Ramanujan meets time-frequency analysis in complicated time series analysis—0
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
Financial Market Trend Forecasting and Performance Analysis Using LSTM—0
The fractal time growth of COVID-19 pandemic: an accurate self-similar model, and urgent conclusions—0
High-dimensional mixed-frequency IV regression—0
Pruned Wasserstein Index Generation Model and wigpy PackageCode0
Difference Attention Based Error Correction LSTM Model for Time Series Prediction—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