Accurate long-term prognostics of proton exchange membrane fuel cells using recurrent and convolutional neural networks

Sahajpal, Kartik; Rana, K.P.S.; Kumar, Vineet

doi:10.1016/j.ijhydene.2023.04.143

Cited by 12 publications

(1 citation statement)

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“…In order to achieve long-term forecasting of time series, researchers have introduced various approaches, including neural networks [6], support vector machines [7], and other machine learning-based methods. Additionally, statistical models such as autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) [8] have also been proposed to analyze long-term data patterns and trends.…”

Section: Introductionmentioning

confidence: 99%

A granularity time series forecasting model combining three-way decision and trend information granularity

Qiu,

Su,

Qian

2024

Preprint

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In recent decades, significant advancements have been made in the field of time series data mining, leading to its widespread application in various domains. However, the existence of data correlation in time series data sets brings challenges to long-term prediction. One approach to address this issue is to transform the original time series into granular time series (GTS). Therefore, a prediction model based on GTS is proposed to meet this challenge in this study. Firstly, the improved L1-trend filtering is used to achieve the optimal segmentation of information particles. Then, a three-way decisions (TWD) model based on shape similarity is used to compress and aggregate information granules. Finally, a GTS prediction model based on LSTM neural network is established. The model effectively retains the trend information of the time series and overcomes the limitation that the existing models cannot adjust the granularity length of the original information. In addition, the proposed model is applied to several real datasets for sensitivity analysis and comparative analysis. The results show that the model has strong performance in long-term forecasting.

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Section: Introductionmentioning

confidence: 99%