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 1676–1700 of 6748 papers

TitleStatusHype
What is the best RNN-cell structure for forecasting each time series behavior?—0
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time SeriesCode0
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment—0
Wasserstein Adversarial Transformer for Cloud Workload PredictionCode1
A Joint-Entropy Approach To Time-series ClassificationCode0
Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators—0
Dual reparametrized Variational Generative Model for Time-Series Forecasting—0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
TiSAT: Time Series Anomaly TransformerCode0
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging—0
A Review of Open Source Software Tools for Time Series Analysis—0
Forecasting the abnormal events at well drilling with machine learning—0
Defending Black-box Skeleton-based Human Activity ClassifiersCode0
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach—0
Geometric Optimisation on Manifolds with Applications to Deep Learning—0
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series—0
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data—0
Contrastive Conditional Neural Processes—0
Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees—0
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
On Robust Inference in Time Series Regression—0
Provably Accurate and Scalable Linear Classifiers in Hyperbolic SpacesCode0
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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