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 2651–2675 of 6748 papers

TitleStatusHype
Cointegration with Occasionally Binding Constraints—0
Few-shot Learning for Time-series Forecasting—0
Enhancing Transformer Efficiency for Multivariate Time Series Classification—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
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence—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
Foundations of Sequence-to-Sequence Modeling for Time Series—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
Causality based Feature Fusion for Brain Neuro-Developmental Analysis—0
A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series—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
Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Causality and Generalizability: Identifiability and Learning Methods—0
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
Finding manoeuvre motifs in vehicle telematics—0
Finding middle ground? Multi-objective Natural Language Generation from time-series data—0
Fourier-RNNs for Modelling Noisy Physics 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