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 2601–2650 of 6748 papers

TitleStatusHype
Fast Partial Fourier Transform—0
Fast Robust Methods for Singular State-Space Models—0
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks—0
Fast-Slow Streamflow Model Using Mass-Conserving LSTM—0
Fast Stability Scanning for Future Grid Scenario Analysis—0
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images—0
Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction—0
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
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods—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
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media—0
A novel method of fuzzy time series forecasting based on interval index number and membership value using support vector machine—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
Ensemble Committees for Stock Return Classification and Prediction—0
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems—0
Approximation algorithms for confidence bands for time series—0
Feature Importance for Time Series Data: Improving KernelSHAP—0
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data—0
Approximation Theory of Convolutional Architectures for Time Series Modelling—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
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization—0
Features or Shape? Tackling the False Dichotomy of Time Series Classification—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves—0
Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach—0
Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs—0
Forecasting the abnormal events at well drilling with machine learning—0
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—0
Enhancing Transformer Efficiency for Multivariate Time Series Classification—0
Federated Reinforcement Learning at the Edge—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence—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
Forecasting The JSE Top 40 Using Long Short-Term Memory Networks—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
Causality based Feature Fusion for Brain Neuro-Developmental Analysis—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
A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series—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