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

Showing 451500 of 6748 papers

TitleStatusHype
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Coherent Probabilistic Aggregate Queries on Long-horizon ForecastsCode1
Transferable Time-Series Forecasting under Causal Conditional ShiftCode1
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionCode1
Unsupervised Change Detection of Extreme Events Using ML On-BoardCode1
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift EstimationCode1
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual DataCode1
Sig-Wasserstein GANs for Time Series GenerationCode1
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systemsCode1
Truth-Conditional Captions for Time Series DataCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Testing and Estimating Structural Breaks in Time Series and Panel Data in StataCode1
ClaSP - Time Series SegmentationCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Neural Flows: Efficient Alternative to Neural ODEsCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
An Empirical Evaluation of Time-Series Feature SetsCode1
SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative ArcsCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Nonlinear proper orthogonal decomposition for convection-dominated flowsCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
On the difficulty of learning chaotic dynamics with RNNsCode1
Dynamical Wasserstein Barycenters for Time-series ModelingCode1
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy GridsCode1
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
TCube: Domain-Agnostic Neural Time-series NarrationCode1
Graph-Guided Network for Irregularly Sampled Multivariate Time SeriesCode1
Long Expressive Memory for Sequence ModelingCode1
Space-Time-Separable Graph Convolutional Network for Pose ForecastingCode1
Truth-Conditional Captioning of Time Series DataCode1
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time SeriesCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Second-Order Neural ODE OptimizerCode1
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution ShiftCode1
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and ForecastingCode1
Random Dilated Shapelet Transform: A New Approach for Time Series ShapeletsCode1
Neural ODE Processes: A Short SummaryCode1
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI ModellingCode1
Fully Spiking Variational AutoencoderCode1
Long-Range Transformers for Dynamic Spatiotemporal ForecastingCode1
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time SeriesCode1
Temporal Convolutional Attention Neural Networks for Time Series ForecastingCode1
Merlion: A Machine Learning Library for Time SeriesCode1
Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google TrendsCode1
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast ModelsCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
Instance-wise Graph-based Framework for Multivariate Time Series ForecastingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified