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 3451–3475 of 6748 papers

TitleStatusHype
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds—0
Predicting Landsat Reflectance with Deep Generative FusionCode0
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems—0
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data AnalysisCode0
Predicting the Future is like Completing a Painting!—0
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models—0
Applying Machine Learning to Crowd-sourced Data from Earthquake DetectiveCode0
Statistical analysis of Wasserstein GANs with applications to time series forecasting—0
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition—0
Predictive Process Model Monitoring using Recurrent Neural Networks—0
Merchant Category Identification Using Credit Card Transactions—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
Tabular Transformers for Modeling Multivariate Time SeriesCode1
Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model—0
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
Time Series Forecasting with Stacked Long Short-Term Memory Networks—0
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic—0
Structured Self-AttentionWeights Encode Semantics in Sentiment AnalysisCode0
ORBITS: Online Recovery of Missing Blocks in Multiple Time Series StreamsCode0
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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