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 3351–3400 of 6748 papers

TitleStatusHype
Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation—0
Self-Similarity Based Time Warping—0
Self-supervised learning for fast and scalable time series hyper-parameter tuning—0
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection—0
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences—0
Semantic of Cloud Computing services for Time Series workflows—0
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems—0
Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models—0
Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks—0
Semi-parametric time series modelling with autocopulas—0
Semi-Supervised Learning for In-Game Expert-Level Music-to-Dance Translation—0
Semi-supervised multiscale dual-encoding method for faulty traffic data detection—0
Semi-supervised Sequential Generative Models—0
Semi-supervised time series classification method for quantum computing—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking—0
Sensor data-driven analysis for identification of causal relationships between exposure to air pollution and respiratory rate in asthmatics—0
Sensor selection on graphs via data-driven node sub-sampling in network time series—0
Sentiment Analysis of Homeric Text: The 1st Book of Iliad—0
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning—0
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention—0
Sequence Prediction Under Missing Data : An RNN Approach Without Imputation—0
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces—0
Sequence to sequence deep learning models for solar irradiation forecasting—0
Sequential asset ranking in nonstationary time series—0
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions—0
Sequential convolutional network for behavioral pattern extraction in gait recognition—0
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains—0
Sequential Monte Carlo With Model Tempering—0
A Joint Model for IT Operation Series Prediction and Anomaly Detection—0
Sequential visibility-graph motifs—0
Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization—0
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
SeriesNet:A Generative Time Series Forecasting Model—0
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting—0
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons—0
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference—0
Shapelets for earthquake detection—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Sharing Features among Dynamical Systems with Beta Processes—0
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models—0
Shortening Time Required for Adaptive Structural Learning Method of Deep Belief Network with Multi-Modal Data Arrangement—0
Use Short Isometric Shapelets to Accelerate Binary Time Series Classification—0
Short note on the behavior of recurrent neural network for noisy dynamical system—0
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data—0
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks—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