About
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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Greatest papers with code
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
• google-research/google-research
• 
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.
INTERPRETABLE MACHINE LEARNING TIME SERIES TIME SERIES FORECASTING
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
• pytorch/fairseq
• 
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
IMAGE CAPTIONING MACHINE TRANSLATION QUESTION GENERATION TEXT GENERATION TIME SERIES
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.
FEATURE IMPORTANCE FEATURE SELECTION TIME SERIES TIME SERIES CLASSIFICATION
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
• microsoft/qlib
•
Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.
sktime: A Unified Interface for Machine Learning with Time Series
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.
TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TIME SERIES FORECASTING
Applications of Deep Neural Networks
• jeffheaton/t81_558_deep_learning
•
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.
Adversarial Robustness Toolbox v1.0.0
• IBM/adversarial-robustness-toolbox
•
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.
GluonTS: Probabilistic Time Series Models in Python
• awslabs/gluon-ts
• 
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
ANOMALY DETECTION TIME SERIES TIME SERIES FORECASTING TIME SERIES PREDICTION
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.
Benchmarking time series classification -- Functional data vs machine learning approaches
In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.