Time Series
Time series deals with sequential data where the data is indexed (ordered) by a time dimension.
( Image credit: Autoregressive CNNs for Asynchronous Time Series )
Leaderboards
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Greatest papers with code
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.
SOTA for Time Series on Amazon
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
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.
SOTA for Time Series on Bitcoin-Alpha
ANOMALY DETECTION TIME SERIES TIME SERIES FORECASTING TIME SERIES PREDICTION
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
#3 best model for
Anomaly Detection
on Numenta Anomaly Benchmark
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
NeurIPS 2019
• Microsoft/EdgeML
•
The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users
Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST'19) 2019
• Microsoft/EdgeML
•
Our in-lab study shows that GesturePod achieves 92% gesture recognition accuracy and can help perform common smartphone tasks faster.
SOTA for Gesture Recognition on GesturePod
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
NeurIPS 2018
• Microsoft/EdgeML
•
FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.
ACTION CLASSIFICATION LANGUAGE MODELLING SPEECH RECOGNITION TIME SERIES TIME SERIES CLASSIFICATION
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
NeurIPS 2018
• Microsoft/EdgeML
•
We propose a method, EMI-RNN, that exploits these observations by using a multiple instance learning formulation along with an early prediction technique to learn a model that achieves better accuracy compared to baseline models, while simultaneously reducing computation by a large fraction.
MULTIPLE INSTANCE LEARNING TIME SERIES TIME SERIES CLASSIFICATION
Optimal Transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
SOTA for Graph Classification on ENZYMES