Open AccessPeramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine
Author(s) -
Bella Audina,
Mohamat Fatekurohman,
Abduh Riski
Publication year - 2021
Publication title -
indonesian journal of applied statistics
Language(s) - English
Resource type - Journals
ISSN - 2621-086X
DOI - 10.13057/ijas.v4i1.47953
Subject(s) - cash flow , support vector machine , series (stratigraphy) , econometrics , investment (military) , operating cash flow , time series , mathematics , variable (mathematics) , finance , computer science , economics , statistics , machine learning , paleontology , mathematical analysis , politics , political science , law , biology
Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the Support Vector Machine (SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the Radial Basic Function kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the Free Cash Flow . The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months. Keywords: cash flow, forecasting, time series, support vector machine.