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 6651–6700 of 6748 papers

TitleStatusHype
Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Fast Variational Inference for Large-scale Internet Diagnosis—0
Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models—0
Bayesian forecast combination using time-varying features—0
Feature-based time-series analysis—0
Feature Construction and Selection for PV Solar Power Modeling—0
Feature Importance for Time Series Data: Improving KernelSHAP—0
Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks—0
Features Fusion Framework for Multimodal Irregular Time-series Events—0
Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents—0
Features or Shape? Tackling the False Dichotomy of Time Series Classification—0
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves—0
Federated Reinforcement Learning at the Edge—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
FedGAN: Federated Generative Adversarial Networks for Distributed Data—0
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers—0
FedST: Secure Federated Shapelet Transformation for Time Series Classification—0
Feedforward Neural Network for Time Series Anomaly Detection—0
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency—0
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification—0
Few-shot Learning for Time-series Forecasting—0
Few-shot time series segmentation using prototype-defined infinite hidden Markov models—0
Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning—0
Filter characteristics in image decomposition with singular spectrum analysis—0
Filtration learning in exact multi-parameter persistent homology and classification of time-series data—0
Economic state classification and portfolio optimisation with application to stagflationary environments—0
Financial Keyword Expansion via Continuous Word Vector Representations—0
Financial Market Trend Forecasting and Performance Analysis Using LSTM—0
Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods—0
Financial series prediction using Attention LSTM—0
Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning—0
Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots—0
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach—0
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks—0
Finding manoeuvre motifs in vehicle telematics—0
Finding middle ground? Multi-objective Natural Language Generation from time-series data—0
Finding Motif Sets in Time Series—0
Finding Patterns in Visualized Data by Adding Redundant Visual Information—0
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks—0
Fine-grained Pattern Matching Over Streaming Time Series—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Fitting very flexible models: Linear regression with large numbers of parameters—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