Churn Prediction with Sequential Data Using Long Short Term Memory

Bayrak, Ahmet Tuğrul; Aktas, Asmin Alev; Susuz, Orkun; Tunalı, Okan

doi:10.1109/ismsit50672.2020.9254679

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Since the impact of COVID-19 on EV charging loads is not instantaneous, this requires predictive models to remember information delivered over longer periods of time. Therefore, this paper will continue to take advantage of the LSTM network's ability to learn long-range dependencies to build an effective EV charging load forecasting model (Bayrak et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Research on electric vehicle load forecasting considering regional special event characteristics

Xie,

Zhang,

Zhang

et al. 2024

Front. Energy Res.

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With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic.

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Section: Introductionmentioning

confidence: 99%

Research on electric vehicle load forecasting considering regional special event characteristics

Xie,

Zhang,

Zhang

et al. 2024

Front. Energy Res.

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show abstract

“…Compared with traditional machine learning [16][17][18] algorithms, the advantage of the vertical federation learning method is that it can use the long and short-term memory [19][20][21] network to predict the future sales of goods in a certain area by integrating the data between social networks, e-commerce platforms, and retailers, on the basis of ensuring that the data are not leaked. Te main contributions of our work are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Federated Learning for Supply Chain Demand Forecasting

Wang

Xie

Duan

et al. 2022

Mathematical Problems in Engineering

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With the country’s policy support and the rapid development of Internet technology, the domestic consumption level has been escalating and the consumption structure has changed. The traditional retail industry cannot integrate all the relevant data due to data security and privacy protection concerns so that it is unable to adjust sales strategies in an accurate and timely manner. New retail has sounded the clarion call for the retail revolution. The supply chain demand forecasting is an important problem for the supply chain management. In this research, we propose a new retail supply chain commodity demand forecasting framework based on vertical federal learning, which solves the problems of data security and privacy faced by new retail theoretically and empirically. In experiments, we use datasets from different platforms (such as social platforms, e-commerce platforms, and retailers) in the same region for federated learning. The experiment results demonstrate the superiority of the proposed algorithm.

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Electric load forecasting based on Long-Short-Term-Memory network via simplex optimizer during COVID-19

Wang

et al. 2022

Energy Reports

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Churn Prediction with Sequential Data Using Long Short Term Memory

Cited by 8 publications

References 4 publications

Research on electric vehicle load forecasting considering regional special event characteristics

Research on electric vehicle load forecasting considering regional special event characteristics

Federated Learning for Supply Chain Demand Forecasting

Electric load forecasting based on Long-Short-Term-Memory network via simplex optimizer during COVID-19

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