Time Series
943 papers with code • 3 benchmarks • 4 datasets
Time series deals with sequential data where the data is indexed (ordered) by a time dimension.
( Image credit: Autoregressive CNNs for Asynchronous Time Series )
Benchmarks
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Speech Commands
Electricity
Unbalance Classification Using Vibration Data
Greatest papers with code
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
google-research/google-research
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Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
pytorch/fairseq
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We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
Distributed and parallel time series feature extraction for industrial big data applications
This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously.
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
microsoft/qlib
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Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.
Applications of Deep Neural Networks
jeffheaton/t81_558_deep_learning
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Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output.
Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks
pytorch/ignite
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The aim of this paper is to introduce two widely applicable regularization methods based on the direct modification of weight matrices.
Adversarial Robustness Toolbox v1.0.0
IBM/adversarial-robustness-toolbox
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Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series
awslabs/gluon-ts
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This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios.
GluonTS: Probabilistic Time Series Models in Python
awslabs/gluon-ts
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We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
Efficient Matrix Profile Computation Using Different Distance Functions
The results also show that the ACAMP algorithm is significantly faster than SCRIMP++ (the state of the art matrix profile algorithm) for the case of z-normalized Euclidean distance.