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
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Forecasting Multi-Dimensional Processes over Graphs—0
Exploring time-series motifs through DTW-SOM—0
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future—0
Random thoughts about Complexity, Data and Models—0
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown—0
Hidden Markov Neural Networks—0
Asset Pricing with General Transaction Costs: Theory and Numerics—0
Characterizing the memory capacity of transmon qubit reservoirs—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Minority Oversampling for Imbalanced Time Series ClassificationCode0
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0—0
The leverage effect and other stylized facts displayed by Bitcoin returns—0
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems—0
On Error Correction Neural Networks for Economic Forecasting—0
Clustering Time Series Data through Autoencoder-based Deep Learning Models—0
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
Flexible Transmitter Network—0
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)—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
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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