title: ÐÐ¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð´ÐµÐ½ÐµÐ¶Ð½Ð¾Ð³Ð¾ Ð¿Ð¾ÑÐ¾ÐºÐ° Ð² Ð±Ð°Ð½ÐºÐ¾Ð¼Ð°ÑÐµ ÑÐ¸Ð¿Ð° cash-out
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Cash flow modeling in the ATM with cash withdrawal function
Creators: name: ÐÑÐºÐ¾Ð²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ ÑÑÐ´Ñ
SARIMA
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
LSTM
ÑÐ½ÑÑÐ¸Ðµ Ð½Ð°Ð»Ð¸ÑÐ½ÑÑ Ð² Ð±Ð°Ð½ÐºÐ¾Ð¼Ð°ÑÐµ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð·Ð°Ð¿Ð°ÑÐ°Ð¼Ð¸
time series
neural networks
atm cash withdrawal
forecasting
inventory management model

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¿ÑÐ¾ÑÐ° Ð½Ð° ÑÐ½ÑÑÐ¸Ðµ Ð½Ð°Ð»Ð¸ÑÐ½ÑÑ Ð´ÐµÐ½ÐµÐ³ Ð² Ð±Ð°Ð½ÐºÐ¾Ð¼Ð°ÑÐµ Ñ ÑÑÐ½ÐºÑÐ¸ÐµÐ¹ Ð²ÑÐ´Ð°ÑÐ¸ Ð½Ð°Ð»Ð¸ÑÐ½ÑÑ Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±Ð°Ð½ÐºÐ¾Ð¼Ð°ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ¾ÑÐ¸Ð¸ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð·Ð°Ð¿Ð°ÑÐ°Ð¼Ð¸ Ð¿ÑÐ¸ Ð½ÐµÐ´ÐµÑÐµÑÐ¼Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¼ ÑÐ¿ÑÐ¾ÑÐµ. ÐÐ»Ð°Ð²Ð° 1 ÑÐ¾Ð´ÐµÑÐ¶Ð¸Ñ Ð¾Ð±Ð·Ð¾Ñ Ð»Ð¸ÑÐµÑÐ°ÑÑÑÑ, Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÑ ÑÐµÑÐ¼Ð¸Ð½Ð¾Ð², Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ ÐºÑÐ¸ÑÐµÑÐ¸ÐµÐ² Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¾Ð±Ð·Ð¾Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¿ÑÐ¾ÑÐ°, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÑ Ð² ÑÐ°Ð±Ð¾ÑÐµ. Ð ÐÐ»Ð°Ð²Ðµ 2 Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð° ÑÐ½ÑÑÐ¸Ñ Ð½Ð°Ð»Ð¸ÑÐ½ÑÑ Ð´ÐµÐ½ÐµÐ¶Ð½ÑÑ ÑÑÐµÐ´ÑÑÐ² Ð½Ð° ÑÑÐ°ÑÐ¸Ð¾Ð½Ð°ÑÐ½Ð¾ÑÑÑ, Ð½Ð°Ð»Ð¸ÑÐ¸Ðµ ÑÑÐµÐ½Ð´Ð°, ÑÐµÐ·Ð¾Ð½Ð½Ð¾ÑÑÐ¸, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÑ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¿ÑÐ¾ÑÐ°. ÐÐ»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¿ÑÐ¾ÑÐ° Ð² RStudio Ð±ÑÐ»Ð° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐµÐ·Ð¾Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð²ÑÐ¾ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ³ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐºÐ¾Ð»ÑÐ·ÑÑÐµÐ³Ð¾ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ (Seasonal ARIMA) Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ñ Ð´Ð¾Ð»Ð³Ð¾Ð¹ ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð¿Ð°Ð¼ÑÑÑÑ LSTM, Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ ÑÐ·ÑÐº Python. ÐÐ»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð¹ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ñ Ð¾ÑÐµÐ½ÐºÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¾ÑÐµÐ½Ð¸Ð²Ð°ÑÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð°. Ð ÐÐ»Ð°Ð²Ðµ ÐÐ°ÐºÐ»ÑÑÐµÐ½Ð¸Ðµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð²ÑÐ²Ð¾Ð´Ñ Ð¿Ð¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ.
description: This work is devoted to comparing the models for forecasting the demand for cash withdrawals at an ATM with the cash withdrawal function for the task of ensuring the efficient operation of an ATM based on the theory of inventory management in non-deterministic demand. Chapter 1 contains a review of the literature, definitions of the main used terms, a description of the criteria for analyzing the time series, and an overview of the demand forecasting models, which were used in the work. Chapter 2 presents the results of the analysis of the time series of cash withdrawals for stationarity, the presence of a trend, seasonality, as well as a description of the construction of models used to forecast demand. To forecast demand a model of seasonal autoregressive integrated moving average (Seasonal ARIMA) was built in the RStudio and a neural network with long-term short-term memory LSTM was built using Python. For each constructed model estimates, which evaluates the quality of the forecasts, were calculated. Chapter Conclusion presents conclusions on the results of the performed work.