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 4451–4500 of 6748 papers

TitleStatusHype
Topological Hidden Markov Models—0
Topological Representational Similarity Analysis in Brains and Beyond—0
Topological Signal Processing using the Weighted Ordinal Partition Network—0
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting—0
Topology, Convergence, and Reconstruction of Predictive States—0
Topology data analysis of critical transitions in financial networks—0
Topology Identification under Spatially Correlated Noise—0
To Post or Not to Post: Using Online Trends to Predict Popularity of Offline Content—0
TOTOPO: Classifying univariate and multivariate time series with Topological Data Analysis—0
To VaR, or Not to VaR, That is the Question—0
Toward a generic representation of random variables for machine learning—0
Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand—0
Towards a Computational Framework for Automated Discovery and Modeling of Biological Rhythms from Wearable Data Streams—0
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—0
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models—0
Towards an AI-based Early Warning System for Bridge Scour—0
Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile—0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series—0
Towards a Rigorous Evaluation of XAI Methods on Time Series—0
Towards a text analysis system for political debates—0
Towards a universal neural network encoder for time series—0
Towards automated symptoms assessment in mental health—0
Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks—0
Towards Better Long-range Time Series Forecasting using Generative Forecasting—0
Towards Building a Political Protest Database to Explain Changes in the Welfare State—0
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection—0
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Towards Experienced Anomaly Detector through Reinforcement Learning—0
Towards Global Crop Maps with Transfer Learning—0
Style Transfer with Time Series: Generating Synthetic Financial Data—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution—0
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks—0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis—0
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children—0
Towards social pattern characterization in egocentric photo-streams—0
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting—0
Towards The Inductive Acquisition of Temporal Knowledge—0
Towards Time-Aware Distant Supervision for Relation Extraction—0
Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns—0
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications—0
Tracking agitation in people living with dementia in a care environment—0
Tracking and tracing in the UK: a dynamic causal modelling study—0
Tracking the Evolution of Words with Time-reflective Text Representations—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
Tractable Dendritic RNNs for Identifying Unknown Nonlinear Dynamical Systems—0
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling—0
Trading styles and long-run variance of asset prices—0
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA—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