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 5501–5525 of 6748 papers

TitleStatusHype
Provably Calibrated Regression Under Distribution Drift—0
Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing—0
PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling—0
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series—0
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback—0
PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm—0
PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction—0
Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic—0
Public Transit Arrival Prediction: a Seq2Seq RNN Approach—0
PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series—0
Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection—0
QIXAI: A Quantum-Inspired Framework for Enhancing Classical and Quantum Model Transparency and Understanding—0
Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis—0
Quadratic Hawkes processes for financial prices—0
Deep Metric Learning Model for Imbalanced Fault Diagnosis—0
Qualitative Assessment of Recurrent Human Motion—0
Quant GANs: Deep Generation of Financial Time Series—0
Quantification in-the-wild: data-sets and baselines—0
Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community—0
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference—0
Quantifying How Hateful Communities Radicalize Online Users—0
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs—0
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain—0
Quantifying Synchronization in a Biologically Inspired Neural Network—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—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