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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 4701–4750 of 6748 papers

TitleStatusHype
Explicit-Duration Markov Switching Models—0
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency—0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
Asset correlation estimation for inhomogeneous exposure pools—0
Multi-Year Vector Dynamic Time Warping Based Crop Mapping—0
InceptionTime: Finding AlexNet for Time Series ClassificationCode1
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal PatternsCode0
Photometric light curves classification with machine learning—0
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms—0
Real Time Trajectory Prediction Using Deep Conditional Generative ModelsCode0
Estimating Granger Causality with Unobserved Confounders via Deep Latent-Variable Recurrent Neural Network—0
Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks—0
Recovery of Future Data via Convolution Nuclear Norm MinimizationCode0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information—0
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)Code0
State Drug Policy Effectiveness: Comparative Policy Analysis of Drug Overdose Mortality—0
Inferring species interactions using Granger causality and convergent cross mappingCode0
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data—0
Reusing Convolutional Activations from Frame to Frame to Speed up Training and InferenceCode0
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future DirectionsCode0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
Multilingual Dynamic Topic Model—0
Interdependency between the Stock Market and Financial News—0
Gaussian mixture model decomposition of multivariate signals—0
Inspection of methods of empirical mode decomposition—0
Machine Learning and the Internet of Things Enable Steam Flood Optimization for Improved Oil Production—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices—0
Early Classification for Agricultural Monitoring from Satellite Time Series—0
The Wiki Music dataset: A tool for computational analysis of popular music—0
Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs—0
Marginally-calibrated deep distributional regression—0
Variationally Inferred Sampling Through a Refined Bound for Probabilistic ProgramsCode0
A framework for anomaly detection using language modeling, and its applications to finance—0
Heterogeneous Relational Kernel Learning—0
Enhanced Cyber-Physical Security through Deep Learning Techniques—0
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms—0
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving—0
Efficient Cross-Validation of Echo State NetworksCode1
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism—0
Quantile Convolutional Neural Networks for Value at Risk Forecasting—0
Detecting Gas Vapor Leaks Using Uncalibrated Sensors—0
A Review of Changepoint Detection Models—0
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling—0
Semi-supervised Sequence Modeling for Elastic Impedance InversionCode0
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction—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