Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

Weytjens, Hans; Lohmann, Enrico; Kleinsteuber, Martin

doi:10.1007/s10660-019-09362-7

Cited by 61 publications

(36 citation statements)

References 12 publications

(14 reference statements)

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“…Thus, this serves to motivate the study of machinelearning techniques within financial forecasting. As an illustration, [15] reviewed corporate cash flow forecasting using account receivable data collected through a specialized accounting software, which provides a richer view of the individual transactions.…”

Section: Conventional Approaches To Financial Forecastingmentioning

confidence: 99%

Automated Data Processing of Bank Statements for Cash Balance Forecasting

Griguta¹,

Gerber

Crockett³

et al. 2021

Lecture Notes in Networks and Systems

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The forecasting of cash inflows and outflows across multiple business operations plays an important role in the financial health of medium and large enterprises. Historically, this function was assigned to specialized treasury departments who projected future cash flows within different business units by processing available information on the expected performance of each business unit (e.g. sales, expenditures). We present an alternative forecasting approach which uses historical cash balance data collected from standard bank statements to systematically predict the future cash positions across different bank accounts. Our main contribution is on addressing challenges in data extraction, curation, and pre-processing, from sources such as digital bank statements. In addition, we report on the initial experiments in using both conventional and machine learning approaches to forecast cash balances. We report forecasting results on both univariate and multivariate, equally-spaced cash balances pertaining to a small, representative subset of bank accounts.

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Section: Conventional Approaches To Financial Forecastingmentioning

confidence: 99%

Automated Data Processing of Bank Statements for Cash Balance Forecasting

Griguta¹,

Gerber

Crockett³

et al. 2021

Lecture Notes in Networks and Systems

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“…As a result of the review of the scientific literature, regarding the implementation of techniques based on Machine Learning, for the approach of predictive solutions supported in time series, it was identified that the “Multi-Layered Perceptron” technique of the category Artificial Neural Networks has produced very good results in different fields of action, such as: investment models based on mutual funds [ 34 , 35 ], epidemiological models [ 35 , 36 ], estimation of the water recharge rate underground [ 37 , 38 ], analysis of the pedals interactions of race car drivers [ 39 ], efficient energy systems based on the prediction of natural gas consumption [ 40 , 41 ], and money flow prediction [ 42 ], among other studies.…”

Section: Contributionsmentioning

confidence: 99%

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

Ariza-Colpas

Ayala-Mantilla²,

Shaheen

et al. 2021

Sensors

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This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.

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“…Network traffic forecasting is made based on network elements and affected by various factors such as the location of network elements, weather, and traffic on different base stations. Forecasting is mainly implemented via Gompertz models or time series forecasting models like LSTM and Prophet models [8,9,10]. The traffic forecast made based on traditional Extended Gompertz Models (EGMs) cannot reflect traffic differences between working days and holidays.…”

Section: Lstm Model For Traffic Forecastingmentioning

confidence: 99%

AI-based network topology optimization system

Zengfu¹,

Jiankun²,

Wang³

et al. 2021

ITU-J FET

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Existing network topology planning does not fully consider the increasing network traffic and problem of uneven link capacity utilization, resulting in lower resource utilization and unnecessary investments in network construction. The AI-based network topology optimization system introduced in this paper builds a Long Short-Term Memory (LSTM) model for time series traffic forecasting, which uses NetworkX, a Python library, for graph analysis, dynamically optimizes the network topology by edge deletion or addition based on traffic over nodes, and ensures network load balancing when node traffic increases, mainly introducing the LSTM forecasting model building process, parameter optimization strategy, and network topology optimization in some detail. As it effectively enhances resource utilization, this system is vital to the optimization of complex network topology. The end of this paper looks forward to the future development of artificial intelligence, and suggests the possibility of how to cooperate with operator networks and how to establish cross-border ecological development.

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Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

Cited by 61 publications

References 12 publications

Automated Data Processing of Bank Statements for Cash Balance Forecasting

Automated Data Processing of Bank Statements for Cash Balance Forecasting

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

AI-based network topology optimization system

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