A Classification Approach to Modeling Financial Time Series

Altilio, Rosa; Andreasi, Giorgio; Panella, Massimo

doi:10.1007/978-3-319-95098-3_9

Cited by 3 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 Number of trainable parameters, including those for bias and batch normalization. 2 Ratio of layer output frequency to system input frequency. 3 Region of influence; see text for detail.…”

Section: Component B-lstm P-cnn P-c/l Ms-cnn Ms-c/lmentioning

confidence: 99%

“…Time series classification is a challenging problem in numerous fields [1], including finance [2], cyber security [3], electronic health record analysis [4], acoustic scene classification [5], and EEG-based brain computer interfaces [6], and it is a central challenge in the field of activity recognition [7]. Numerous time series classification algorithms have been proposed [8], and the diversity of time series classification problems is evident in dataset repositories such as the UCR Time Series Archive [9] or the UCI Machine Learning Repository [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chambers¹,

Yoder²

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Component B-lstm P-cnn P-c/l Ms-cnn Ms-c/lmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chambers¹,

Yoder²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Time series classification is a challenging problem in numerous fields [1], including finance [2], cyber security [3], electronic health record analysis [4], acoustic scene classification [5], and electroencephalogram (EEG)-based brain computer interfaces [6], and it is a central challenge in the field of activity recognition [7]. Numerous time series classification algorithms have been proposed [8,9], and the diversity of time series classification problems is evident in dataset repositories, such as the UCR Time Series Archive [10] or the UCI Machine Learning Repository [11].…”

Section: Introductionmentioning

confidence: 99%

“…Number of trainable parameters, including those for bias and batch normalization 2. Ratio of layer output frequency to system input frequency 3.…”

mentioning

confidence: 99%

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

Chambers¹,

Yoder²

2020

Sensors

View full text Add to dashboard Cite

In this paper, 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 convolutional neural network (CNN) and long short-term memory (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 when applied to the benchmarked dataset, and it can be extensively customized for other applications.

show abstract

“…Time series classification is a challenging problem in numerous fields [1], including finance [2], cyber security [3], electronic health record analysis [4], acoustic scene classification [5], and EEGbased brain computer interfaces [6], and it is a central challenge in the field of activity recognition [7]. Numerous time series classification algorithms have been proposed [8] [9], and the diversity of time series classification problems is evident in dataset repositories such as the UCR Time Series Archive [10] or the UCI Machine Learning Repository [11].…”

Section: Introductionmentioning

confidence: 99%

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

Chambers¹,

Yoder²

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

A Classification Approach to Modeling Financial Time Series

Cited by 3 publications

References 18 publications

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

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

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

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

Contact Info

Product

Resources

About