Time Series
Time series deals with sequential data where the data is indexed (ordered) by a time dimension.
State-of-the-art leaderboards
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.
IMAGE CAPTIONING MACHINE TRANSLATION 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.
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.
Adversarial Robustness Toolbox v0.4.0
• IBM/adversarial-robustness-toolbox
The Adversarial Robustness Toolbox (ART) is a Python library designed to support researchers and developers in creating novel defence techniques, as well as in deploying practical defences of real-world AI systems.
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
Real numbers, data science and chaos: How to fit any dataset with a single parameter
We show how any dataset of any modality (time-series, images, sound...) can be approximated by a well-behaved (continuous, differentiable...) scalar function with a single real-valued parameter.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
• chickenbestlover/RNN-Time-series-Anomaly-Detection
•
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ICLR 2019 • JustGlowing/minisom
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
DIMENSIONALITY REDUCTION REPRESENTATION LEARNING TIME SERIES
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years
Time-series of satellite images may reveal important data about changes in environmental conditions and natural or urban landscape structures that are of potential interest to citizens, historians, or policymakers.