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 2451–2500 of 6748 papers

TitleStatusHype
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity DemandCode0
Bayesian Online Change Point Detection for Baseline Shifts—0
An Improved Mathematical Model of Sepsis: Modeling, Bifurcation Analysis, and Optimal Control Study for Complex Nonlinear Infectious Disease System—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Applications of Signature Methods to Market Anomaly Detection—0
Churn prediction in online gambling—0
Unifying Epidemic Models with Mixtures—0
Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey—0
Approximate Factor Models for Functional Time SeriesCode0
Second-Order Ultrasound Elastography with L1-norm Spatial Regularization—0
Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics—0
Sales Time Series Analytics Using Deep Q-Learning—0
Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting—0
Bitcoin Price Predictive Modeling Using Expert Correction—0
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies—0
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics—0
A Review of Mathematical and Computational Methods in Cancer Dynamics—0
Elastic Product Quantization for Time SeriesCode0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics—0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features—0
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series—0
Modelling matrix time series via a tensor CP-decomposition—0
Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach—0
Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities—0
An Efficient Federated Distillation Learning System for Multi-task Time Series Classification—0
ChunkFormer: Learning Long Time Series with Multi-stage Chunked Transformer—0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations—0
Monte Carlo EM for Deep Time Series Anomaly DetectionCode0
Time-Incremental Learning from Data Using Temporal Logics—0
Cognitive Computing to Optimize IT Services—0
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate PredictionCode0
Time Series Data Mining Algorithms Towards Scalable and Real-Time Behavior Monitoring—0
Toeplitz Least Squares Problems, Fast Algorithms and Big Data—0
Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series—0
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network—0
Stationarity analysis of the stock market data and its application to mechanical trading—0
Neural Echo State Network using oscillations of gas bubbles in water—0
Collaborative adversary nodes learning on the logs of IoT devices in an IoT network—0
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series—0
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version—0
PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
SSDNet: State Space Decomposition Neural Network for Time Series ForecastingCode0
Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution—0
Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEGCode0
Discrete signature and its application to financeCode0
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models—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