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 4126–4150 of 6748 papers

TitleStatusHype
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative TeacherCode1
Kernels for time series with irregularly-spaced multivariate observations—0
Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective—0
A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models—0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Predictability of Power Grid Frequency—0
Exploring time-series motifs through DTW-SOM—0
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic ForecastingCode1
Forecasting Multi-Dimensional Processes over Graphs—0
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future—0
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown—0
Random thoughts about Complexity, Data and Models—0
Characterizing the memory capacity of transmon qubit reservoirs—0
Asset Pricing with General Transaction Costs: Theory and Numerics—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Hidden Markov Neural Networks—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
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
The leverage effect and other stylized facts displayed by Bitcoin returns—0
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0—0
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems—0
Clustering Time Series Data through Autoencoder-based Deep Learning Models—0
On Error Correction Neural Networks for Economic Forecasting—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