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Time Series Analysis

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하위 태스크: 12
평가표: 3
보존된 평가 기록: 14

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.

<span style="color:grey; opacity: 0.6">( Image credit: Autoregressive CNNs for Asynchronous Time Series )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/aryan-jadon/regression-loss-functions-in-time-series-forecasting-tensorflow

공식 코드

A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting

https://github.com/asanctvs/structure-identification

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Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach

https://github.com/asarantsev/BayesianLT

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A New Valuation Measure for the Stock Market

https://github.com/ashfarhangi/aa-forecast

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AA-Forecast: Anomaly-Aware Forecast for Extreme Events

https://github.com/ashfarhangi/covid-19

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A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19

https://github.com/ashwinkalyan/dbs

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Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

https://github.com/askoki/nfl_dpi_prediction

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ML-Based Approach for NFL Defensive Pass Interference Prediction Using GPS Tracking Data

https://github.com/astha-chem/mvts-ano-eval

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An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series

https://github.com/astrolabsoftware/fink-science

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Finding active galactic nuclei through Fink

https://github.com/atifraza/random-shapelet-ensembles

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Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs

https://github.com/atik-ahamed/timemachine

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TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

https://github.com/atik666/augmmentationcode

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Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data

https://github.com/autogluon/autogluon

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AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

https://github.com/automl/Auto-PyTorch

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Efficient Automated Deep Learning for Time Series Forecasting

https://github.com/automl/mamba4cast

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Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models

https://github.com/avishaiwa/SPARCWave

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Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation

https://github.com/awikner/denpool

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Attention-Based Ensemble Pooling for Time Series Forecasting

https://github.com/awslabs/gluon-ts

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PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series

https://github.com/awslabs/gluon-ts/tree/master/src/gluonts/nursery/spliced_binned_pareto

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Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series

https://github.com/awslabs/gluonts

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Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms

https://github.com/ayaabdelsalam91/Input-Cell-Attention

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Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks

https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark

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Benchmarking Deep Learning Interpretability in Time Series Predictions

https://github.com/ayaabdelsalam91/saliency_guided_training

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Improving Deep Learning Interpretability by Saliency Guided Training

https://github.com/azencot-group/gatlm

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Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning

https://github.com/azencot-group/kann

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An Operator Theoretic Approach for Analyzing Sequence Neural Networks

https://github.com/b-deforce/tsa-on-autopilot

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TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection

https://github.com/bachnguyente/temporal-mgn

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Temporal Multiresolution Graph Neural Networks For Epidemic Prediction

https://github.com/bachtranxuan/TPS

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Dynamic transformation of prior knowledge into Bayesian models for data streams

https://github.com/badripatro/heracles

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Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis

https://github.com/badripatro/mamba360

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Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

https://github.com/badripatro/sct

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Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis

https://github.com/bagrow/recovering-information-temporal-networks

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Recovering lost and absent information in temporal networks

https://github.com/baiyimeng/labelcraft

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LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

https://github.com/bamler-lab/understanding-ans

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Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective

https://github.com/barahona-research-group/BayesFactorSimilarity

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Similarity measure for sparse time course data based on Gaussian processes

https://github.com/baraline/convst

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Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets

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