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Time Series

156 papers with code · Time Series

Time series deals with sequential data where the data is indexed (ordered) by a time dimension.

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Greatest papers with code

Distributed and parallel time series feature extraction for industrial big data applications

25 Oct 2016blue-yonder/tsfresh

The all-relevant problem of feature selection is the identification of all strongly and weakly relevant attributes. 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.

TIME SERIES TIME SERIES CLASSIFICATION

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

7 Oct 2016facebookresearch/fairseq-py

Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

IMAGE CAPTIONING MACHINE TRANSLATION TIME SERIES

Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

12 Oct 2015numenta/NAB

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. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.

ANOMALY DETECTION TIME SERIES

Adversarial Robustness Toolbox v0.4.0

3 Jul 2018IBM/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. The Adversarial Robustness Toolbox supports machine learning models (and deep neural networks (DNNs) specifically) implemented in any of the most popular deep learning frameworks (TensorFlow, Keras, PyTorch and MXNet).

TIME SERIES

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

ICLR 2019 JustGlowing/minisom

To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. 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

29 Mar 2018JustGlowing/minisom

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. We applied a fast method of image analysis using Self Organized Maps (SOM) and, more specifically, the quantization error (QE), for the visualization of critical changes in satellite images of Las Vegas, generated across the years 1984-2008, a period of major restructuration of the urban landscape.

TIME SERIES

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

19 Nov 2015pbashivan/EEGLearn

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task.

TIME SERIES VIDEO CLASSIFICATION

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

1 Jul 2016chickenbestlover/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. We show that EncDec-AD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series.

ANOMALY DETECTION TIME SERIES

Multitask Learning and Benchmarking with Clinical Time Series Data

22 Mar 2017yerevann/mimic3-benchmarks

Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets.

TIME SERIES

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

20 Nov 2016cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting.

TIME SERIES TIME SERIES CLASSIFICATION