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

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Showing 3501–3550 of 6748 papers

TitleStatusHype
Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series—0
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures—0
Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso—0
Probabilistic Programming with Gaussian Process Memoization—0
Probabilistic Reconciliation of Count Time Series—0
Efficient probabilistic reconciliation of forecasts for real-valued and count time series—0
Probabilistic Segmentation via Total Variation Regularization—0
Probabilistic structure discovery in time series data—0
Probabilistic Temporal Subspace Clustering—0
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments—0
Probabilistic Transformer For Time Series Analysis—0
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models—0
Probabilistic water demand forecasting using quantile regression algorithms—0
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information—0
Process Knowledge Driven Change Point Detection for Automated Calibration of Discrete Event Simulation Models Using Machine Learning—0
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems—0
Process Monitoring Using Maximum Sequence Divergence—0
Process Outcome Prediction: CNN vs. LSTM (with Attention)—0
Production Function of the Mining Sector of Iran—0
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons—0
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles—0
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective—0
Prognostic classification based on random convolutional kernel—0
Progressive Cross-modal Knowledge Distillation for Human Action Recognition—0
Progressive Fusion for Multimodal Integration—0
Progressive Growing of Neural ODEs—0
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences—0
Projection assisted Dynamic Mode Decomposition of large scale data—0
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Proofs and additional experiments on Second order techniques for learning time-series with structural breaks—0
Propagation Graph Estimation from Individual's Time Series of Observed States—0
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
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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