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 2651–2700 of 6748 papers

TitleStatusHype
Cointegration with Occasionally Binding Constraints—0
Few-shot Learning for Time-series Forecasting—0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
Few-shot time series segmentation using prototype-defined infinite hidden Markov models—0
Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs—0
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine 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
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—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
Enhancing Transformer Efficiency for Multivariate Time Series Classification—0
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence—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
Causality based Feature Fusion for Brain Neuro-Developmental Analysis—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series—0
Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method—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
Causality and Generalizability: Identifiability and Learning Methods—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
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
From Generalization Analysis to Optimization Designs for State Space Models—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification—0
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study—0
Causal Inference via Kernel Deviance Measures—0
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data—0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Flexible conditional density estimation for time series—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Flexible Transmitter Network—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
A regression model with a hidden logistic process for feature extraction from time series—0
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
Causal Inference from Slowly Varying Nonstationary Processes—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