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 3401–3425 of 6748 papers

TitleStatusHype
Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy Rules: Application to Simulations of Speed Control of an Automobile—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs—0
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data—0
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment—0
Portfolio Risk Assessment using Copula Models—0
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs—0
Position: Empowering Time Series Reasoning with Multimodal LLMs—0
Positive blood culture detection in time series data using a BiLSTM network—0
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application—0
Potential Conditional Mutual Information: Estimators, Properties and Applications—0
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM—0
Power Line Communication and Sensing Using Time Series Forecasting—0
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning—0
PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction—0
Practical Data-Dependent Metric Compression with Provable Guarantees—0
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN—0
Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs—0
PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations—0
Predictability of Power Grid Frequency—0
Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach—0
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability—0
Predicting China's CPI by Scanner Big 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