Preprint Article Version 1 This version is not peer-reviewed

FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification

Version 1 : Received: 21 February 2020 / Approved: 23 February 2020 / Online: 23 February 2020 (11:00:10 CET)
Version 2 : Received: 14 April 2020 / Approved: 15 April 2020 / Online: 15 April 2020 (08:02:43 CEST)

A peer-reviewed article of this Preprint also exists.

Chambers, R.D.; Yoder, N.C. FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification. Sensors 2020, 20, 2498. Chambers, R.D.; Yoder, N.C. FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification. Sensors 2020, 20, 2498.

Journal reference: Sensors 2020, 20
DOI: 10.3390/s20092498

Abstract

We present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular CNN and CNN-LSTM motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics on the benchmarked dataset, and it can be extensively customized for other applications.

Subject Areas

activity recognition; time series classification; neural; networks; deep learning; machine learning; CNNs; LSTMs; many-to-many

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