Mcfly: Automated deep learning on time series

Kuppevelt, Dafne van; Meijer, Christiaan; Huber, Florian; Ploeg, Auke van der; Georgievska, Sonja; Hees, Vincent T. van

doi:10.1016/j.softx.2020.100548

Cited by 36 publications

(27 citation statements)

References 16 publications

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“…Below we provide a description of four architectures used for time series classification: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Residual Networks (ResNet) and Inception Time Networks (InceptionTime). These architectures were chosen because they are widely adopted for time series classification and because they are available in mcfly (the software we use here for model implementation; van Kuppevelt et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Deep learning models can be implemented using several programming languages and specialised libraries (see Christin et al, 2019 for a review). Here, we use mcfly, a Python package for time series classification using deep learning (van Kuppevelt et al, 2020). This package is aimed at non-experts and it should be easy to use for ‘mid-level’ ecological modellers.…”

Section: Methodsmentioning

confidence: 99%

“…In its current version (v.3.0) mcfly allows implementing CNN, Deep convolutional LSTM ('DeepConvLSTM'; an architecture composed of convolutional and LSTM recurrent layers), ResNet and InceptionTime architectures. Specific details about the components and structure of each architecture are given in van Kuppevelt et al (2020).…”

Section: A Standardized Accessible Deep Learning Modelling Frameworkmentioning

confidence: 99%

“…First, we perform species identification based on recordings of insect wing flap movements, second, we predict the potential distribution of a vulnerable mammal species using time series of monthly climate data, and third we predict the seasonal patterns of fruiting of a mushroom species, based on meteorological time series. We implement all models using ‘mcfly’ (van Kuppevelt et al, 2020), a Python package aimed at time series classification for non-experts in deep learning, and which should be accessible to the generality of ecological modelers.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for supervised classification of temporal data in ecology

Capinha

Ceia-Hasse²,

Kramer

et al. 2020

Preprint

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1. Time series classification consists of assigning time series into one of two or more predefined classes. This procedure plays a role in a vast number of ecological classification tasks, including species identification, animal behaviour analysis, predictive mapping, or the detection of critical transitions in ecological systems. In ecology, the usual approach to time series classification consists of transforming the time series into static predictors and then using these in conventional statistical or machine learning models. However, recent deep learning approaches now enable the classification using the raw time series data, avoiding the need for domain expertise, eliminating subjective and resource-consuming data transformation procedures, and potentially improving classification results. 2. We here introduce ecologists to time series classification using deep learning models. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problem at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) species identification from wingbeat spectrograms; ii) species distribution modelling from time series of climatic variables and iii) the classification of phenological phases from continuous meteorological data. 3. The deep learning approach delivered ecologically robust and high performing classifications for the three case studies. The results obtained also allowed us to point future research directions and highlight current limitations. 4. We demonstrate the high potential and wide applicability of deep learning for time series classification in ecology. We recommend this approach be considered as an alternative to commonly used techniques requiring the transformation of time series data.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Standardized Accessible Deep Learning Modelling Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for supervised classification of temporal data in ecology

Capinha

Ceia-Hasse²,

Kramer

et al. 2020

Preprint

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“…A random search over the hyperparameters, using the Mcfly software package [33], was performed to generate 50 sets of convolutional neural network (CNN) [34] models and 50 sets of deep learning convolutional, long-short-term, memory (DeepConvLSTM) [35] models. The randomly selected hyperparameters were regularization rate, learning rate, number of convolution layers, number of filters per layer, number of hidden nodes, and number of long-short-term memory (LSTM) layers (only for DeepConvLSTM) [33]. Some 70% of the training/validation dataset was randomly used to train the total of 100 models, and the remaining 30% was used to evaluate the models.…”

Section: Deep Learning Algorithm Training and Selectionmentioning

confidence: 99%

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data

Haghayegh

Khoshnevis

Smolensky

et al. 2020

Sensors

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Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3–6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2–34.3% higher than comparator IAs), and 58.7% Kappa agreement (16–23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters—sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.

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