A Transfer Learning Scheme for Time Series Forecasting Using Facebook Prophet

Warushavithana, Menuka; Mitra, Saptashwa; Arabi, Mazdak; Breidt, Jay; Pallickara, Shrideep

doi:10.1109/cluster48925.2021.00076

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…However, despite the novelty of the research field, time series-based TL has already been applied in various fields like manufacturing [15], finance [16], geoscience [17], mobility [18], and medicine [19]. Successfully solved tasks include time series imaging [20], anomaly detection [21], classification [22], and forecasting [23]. A seminal work applying TL to time series classification analyzed how the pretraining of a model with a different source datasets affects the classification accuracy of the target task [24].…”

Section: Transfer Learning For Time Seriesmentioning

confidence: 99%

Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles

Gaugel¹,

Reichert²

2023

Preprint

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Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

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Section: Transfer Learning For Time Seriesmentioning

confidence: 99%

Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles

Gaugel¹,

Reichert²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, despite the novelty of the research field, time series-based TL has already been applied in various fields like manufacturing [ 15 ], finance [ 16 ], geoscience [ 17 ], mobility [ 18 ], and medicine [ 19 ]. Successfully solved tasks include time series imaging [ 20 ], anomaly detection [ 21 ], classification [ 22 ], and forecasting [ 23 ]. A seminal work applying TL to time series classification analyzed how the pretraining of a model with different source datasets affects the classification accuracy of the target task [ 24 ].…”

Section: Literature Researchmentioning

confidence: 99%

Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles

Gaugel

Reichert

2023

Sensors

View full text Add to dashboard Cite

Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and has received much attention in recent industrial research. This paper focuses on the problem of time series segmentation and presents the first in-depth research on transfer learning for deep learning-based time series segmentation on the industrial use case of end-of-line pump testing. In particular, we investigate whether the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models with respect to accuracy and training speed. The benefit can be most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative transfer learning were observed as well. Thus, the research emphasizes the potential, but also the challenges, of industrial transfer learning.

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