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 4926–4950 of 6748 papers

TitleStatusHype
Using Clinical Notes for ICU Management—0
On Policy Evaluation with Aggregate Time-Series Shocks—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018—0
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in DrosophilaCode0
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting—0
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series—0
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends—0
Efficient Covariance Estimation from Temporal DataCode0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Monotonic Gaussian Process FlowCode0
Learning the Non-linearity in Convolutional Neural Networks—0
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriesCode1
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Graph-based era segmentation of international financial integration—0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
Deep Factors for Forecasting—0
BreizhCrops: A Time Series Dataset for Crop Type MappingCode0
Evaluating time series forecasting models: An empirical study on performance estimation methodsCode0
Kernel Estimation for Panel Data with Heterogeneous Dynamics—0
TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation—0
ODE^2VAE: Deep generative second order ODEs with Bayesian neural networksCode0
Topological Data Analysis of Time Series Data for B2B Customer Relationship Management—0
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided EmbeddingCode0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—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