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 4251–4275 of 6748 papers

TitleStatusHype
Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting—0
What is the best RNN-cell structure for forecasting each time series behavior?—0
What Makes An Asset Useful?—0
What went wrong and when? Instance-wise Feature Importance for Time-series Models—0
What went wrong and when?\\ Instance-wise feature importance for time-series black-box models—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming—0
When is Early Classification of Time Series Meaningful?—0
When Ramanujan meets time-frequency analysis in complicated time series analysis—0
When Traffic Flow Prediction Meets Wireless Big Data Analytics—0
Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits—0
Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?—0
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals—0
Wind power ramp prediction algorithm based on wavelet deep belief network—0
Wind speed forecast using random forest learning method—0
Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Winning the ICCV 2019 Learning to Drive Challenge—0
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands—0
Winterization of Texan power system infrastructure is profitable but risky—0
WISDoM: characterizing neurological timeseries with the Wishart distribution—0
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams—0
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index—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