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 4151–4175 of 6748 papers

TitleStatusHype
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)Code1
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)—0
Flexible Transmitter Network—0
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs—0
Probabilistic Spatial Transformer NetworksCode0
Challenges in Forecasting Malicious Events from Incomplete Data—0
TSInsight: A local-global attribution framework for interpretability in time-series data—0
Forecasting in multivariate irregularly sampled time series with missing values—0
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov ModelCode1
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—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
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder—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
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
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
When Ramanujan meets time-frequency analysis in complicated time series analysis—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