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 5601–5650 of 6748 papers

TitleStatusHype
Can automated smoothing significantly improve benchmark time series classification algorithms?—0
Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering—0
Canonical Time Warping for Alignment of Human Behavior—0
Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19—0
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?—0
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?—0
Can we imitate the principal investor's behavior to learn option price?—0
Capacity of the covariance perceptron—0
Cap or No Cap? What Can Governments Do to Promote EV Sales?—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
Forecasting Cardiology Admissions from Catheterization Laboratory—0
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data—0
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application—0
Case-based reasoning for rare events prediction on strategic sites—0
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection—0
CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction—0
Cats & Co: Categorical Time Series Coclustering—0
Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic—0
Causal analysis of Covid-19 Spread in Germany—0
Causal Analysis of Generic Time Series Data Applied for Market Prediction—0
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction—0
Causal Compression—0
Causal Consistency of Structural Equation Models—0
Causal Digital Twin from Multi-channel IoT—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Causal Discovery from Conditionally Stationary Time Series—0
Causal Discovery from Sparse Time-Series Data Using Echo State Network—0
Causal Discovery from Subsampled Time Series Data by Constraint Optimization—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Causal Hidden Markov Model for Time Series Disease Forecasting—0
Causal impact of severe events on electricity demand: The case of COVID-19 in Japan—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Causal inference for climate change events from satellite image time series using computer vision and deep learning—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Causal Inference via Kernel Deviance Measures—0
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship—0
Causality and Generalizability: Identifiability and Learning Methods—0
Causality based Feature Fusion for Brain Neuro-Developmental Analysis—0
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—0
Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs—0
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems—0
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Causal Triple Attention Time Series Forecasting—0
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations—0
Cause-Effect Preservation and Classification using Neurochaos Learning—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