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 551–575 of 6748 papers

TitleStatusHype
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Efficient Cross-Validation of Echo State NetworksCode1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly BenchmarkCode1
Evaluation of deep learning models for multi-step ahead time series predictionCode1
AGNet: Weighing Black Holes with Deep LearningCode1
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering AlgorithmCode1
AGNet: Weighing Black Holes with Machine LearningCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Automated Evolutionary Approach for the Design of Composite Machine Learning PipelinesCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Automatic Change-Point Detection in Time Series via Deep LearningCode1
FastDTW is approximate and Generally Slower than the Algorithm it ApproximatesCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution TestsCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
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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