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 2751–2800 of 6748 papers

TitleStatusHype
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs—0
Three Remarks On Asset Pricing—0
Inferring Temporal Logic Properties from Data using Boosted Decision Trees—0
Matched Illumination Waveforms using Multi-Tone Sinusoidal Frequency Modulation—0
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Arbitrage-free neural-SDE market modelsCode1
Can we imitate the principal investor's behavior to learn option price?—0
Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning—0
Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing—0
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application—0
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.Code1
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
RFID-based Article-to-Fixture Predictions in Real-World Fashion Stores—0
Entropy-based Discovery of Summary Causal Graphs in Time Series—0
Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series—0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Periodic Freight Demand Estimation for Large-scale Tactical Planning—0
Learning Representations for Incomplete Time Series Clustering—0
The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries—0
Identification robust inference for moments based analysis of linear dynamic panel data models—0
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity RecognitionCode0
Univariate Long-Term Municipal Water Demand Forecasting—0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices—0
Learning Disentangled Representations for Time Series—0
Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks—0
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting—0
Uncertainty Measurement of Basic Probability Assignment Integrity Based on Approximate Entropy in Evidence Theory—0
Classifying Contaminated Cell Cultures using Time Series Features—0
Joint learning of multiple Granger causal networks via non-convex regularizations: Inference of group-level brain connectivityCode0
Quantified Sleep: Machine learning techniques for observational n-of-1 studiesCode1
Long Short-term Memory RNN—0
Monash Time Series Forecasting ArchiveCode1
Edges in Brain Networks: Contributions to Models of Structure and Function—0
Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric ClassificationCode1
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Estimation and Quantization of Expected Persistence Diagrams—0
Bayesian inference and superstatistics to describe long memory processes of financial time series—0
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series ForecastingCode1
SIRNN: A Math Library for Secure RNN InferenceCode1
Symbol Dynamics, Information theory and Complexity of Economic time series—0
On projection methods for functional time series forecastingCode0
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data—0
The effects of regularisation on RNN models for time series forecasting: Covid-19 as an exampleCode0
Segmenting Hybrid Trajectories using Latent ODEsCode0
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification—0
COVID-19: The extraction of the effective reproduction number from the time series of new cases—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