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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 791–800 of 6748 papers

Title	Date	Tasks	Status	Hype
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks	Jul 16, 2021	Cloud RemovalEarth Observation	CodeCode Available	1
Parameter Efficient Deep Probabilistic Forecasting	Dec 6, 2021	Probabilistic Time Series ForecastingTime Series	CodeCode Available	1
Can Multimodal LLMs Perform Time Series Anomaly Detection?	Feb 25, 2025	Anomaly DetectionIrregular Time Series	CodeCode Available	1
Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric Classification	May 13, 2021	Classificationfeature selection	CodeCode Available	1
Phase2vec: Dynamical systems embedding with a physics-informed convolutional network	Dec 7, 2022	Time SeriesTime Series Analysis	CodeCode Available	1
Chaos as an interpretable benchmark for forecasting and data-driven modelling	Oct 11, 2021	BenchmarkingSymbolic Regression	CodeCode Available	1
Benchmark time series data sets for PyTorch -- the torchtime package	Jul 25, 2022	Time SeriesTime Series Analysis	CodeCode Available	1
Benchmarking Deep Learning Interpretability in Time Series Predictions	Oct 26, 2020	BenchmarkingDeep Learning	CodeCode Available	1
Automatic Change-Point Detection in Time Series via Deep Learning	Nov 7, 2022	Change Point DetectionDeep Learning	CodeCode Available	1
Bilinear Input Normalization for Neural Networks in Financial Forecasting	Sep 1, 2021	Time SeriesTime Series Analysis	CodeCode Available	1

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All datasets PhysioNet Challenge 2012 Speech Commands Ventilator Pressure Prediction

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	naive classifier	F1	87.47	—	Unverified
2	GRU-D - APC (n = 1)	F1	27.3	—	Unverified
3	GRU-APC (n = 1)	F1	25.7	—	Unverified
4	GRU-D	F1	22.5	—	Unverified
5	GRU	F1	22.3	—	Unverified
6	GRU-Simple	F1	22.2	—	Unverified
7	GRU-Mean	F1	22.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SepTr	% Test Accuracy	98.51	—	Unverified
2	ViT	% Test Accuracy	98.11	—	Unverified
3	FlexTCN-4	% Test Accuracy	97.73	—	Unverified
4	MatchboxNet	% Test Accuracy	97.4	—	Unverified
5	CKCNN (100k)	% Test Accuracy	95.27	—	Unverified
6	FlexTCN-6	% Test Accuracy (Raw Data)	91.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResBiLSTM	MAE	0.13	—	Unverified