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 551–575 of 6748 papers

TitleStatusHype
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
Motion ID: Human Authentication Approach—0
Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram—0
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular DataCode2
Recursive deep learning framework for forecasting the decadal world economic outlookCode0
Neuronal architecture extracts statistical temporal patternsCode0
Parameterizing the cost function of Dynamic Time Warping with application to time series classification—0
Multi-view Kernel PCA for Time series Forecasting—0
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the EdgeCode1
WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series ClassificationCode1
Context-specific kernel-based hidden Markov model for time series analysisCode0
Chemical Integration of ODEs using Idealized Abstract SolutionsCode0
Flexible conditional density estimation for time series—0
A Framework for Evaluating the Impact of Food Security Scenarios—0
Learning Reservoir Dynamics with Temporal Self-Modulation—0
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time SeriesCode1
LSTM and CNN application for core-collapse supernova search in gravitational wave real data—0
ECGAN: Self-supervised generative adversarial network for electrocardiography—0
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological DataCode1
Ti-MAE: Self-Supervised Masked Time Series AutoencodersCode1
Estimation of Sea State Parameters from Ship Motion Responses Using Attention-based Neural Networks—0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Regular Time-series Generation using SGM—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