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 1951–1975 of 6748 papers

TitleStatusHype
Towards Similarity-Aware Time-Series ClassificationCode1
A Review of Mathematical and Computational Methods in Cancer Dynamics—0
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics—0
Elastic Product Quantization for Time SeriesCode0
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic DataCode1
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics—0
The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features—0
Financial time series forecasting with multi-modality graph neural networkCode1
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor DataCode1
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series—0
Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities—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
An Efficient Federated Distillation Learning System for Multi-task Time Series Classification—0
ChunkFormer: Learning Long Time Series with Multi-stage Chunked Transformer—0
An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations—0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
Monte Carlo EM for Deep Time Series Anomaly DetectionCode0
Cognitive Computing to Optimize IT Services—0
Time-Incremental Learning from Data Using Temporal Logics—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
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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