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 6501–6525 of 6748 papers

TitleStatusHype
Adversarial Attacks on Deep Models for Financial Transaction RecordsCode0
Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture modelsCode0
ProActive: Self-Attentive Temporal Point Process Flows for Activity SequencesCode0
Classification of Time-Series Images Using Deep Convolutional Neural NetworksCode0
Trainable Time Warping: Aligning Time-Series in the Continuous-Time DomainCode0
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial ForensicsCode0
Modeling The Intensity Function Of Point Process Via Recurrent Neural NetworksCode0
Deep learning-based deconvolution for interferometric radio transient reconstructionCode0
Probabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price PredictionCode0
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based ModelCode0
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGANCode0
Deep learning approach to Fourier ptychographic microscopyCode0
Deep Learning Algorithms for Hedging with FrictionsCode0
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series PredictionCode0
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving GraphsCode0
Concurrent Neural Network : A model of competition between times seriesCode0
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary PerspectiveCode0
Deep Kalman FiltersCode0
Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem modelCode0
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time SeriesCode0
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix FactorizationCode0
Wikipedia graph mining: dynamic structure of collective memoryCode0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Automated data-driven approach for gap filling in the time series using evolutionary learningCode0
Modelling stellar activity with Gaussian process regression networksCode0
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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