Hello!

Time series analysis is a good technique but I think it is not applicable in your case. Cash withdrawals from an ATM depend not only on time.

Try to find periodic variations (seasonality), e.g. (1) every Friday people withdraw money for the weekend or (2) between 25-27th day of each month people get their wages or (3) between 7-10th day of each month some people get their retired payment.

Using a neural network is a good idea. You may define 31 output variables – to predict cash withdrawals for each day of the next month.

Firstly, there are a difference between ATM with depositing cash function, or without this function.

In the first case, you need to re-run for each day and for each ATM,  all transactions with the respect of operations time, and then measure the min level, which can be considered as the ATM need of the day.

Using this information for all days, you can run a distibution fitting (for example using Matlab) and measure the ATM demand which satisfy 95% or 99% of customer's needs

Try with Wavelet neural network for time series analysis

Hello!

Time series analysis is a good technique but I think it is not applicable in your case. Cash withdrawals from an ATM depend not only on time.

Try to find periodic variations (seasonality), e.g. (1) every Friday people withdraw money for the weekend or (2) between 25-27th day of each month people get their wages or (3) between 7-10th day of each month some people get their retired payment.

Using a neural network is a good idea. You may define 31 output variables – to predict cash withdrawals for each day of the next month.

Dear Mr.  Mayur Narkhede

I suggest Autoregressive Intregrated Moving Average (ARIMA ) model for forecasting the cash demand for the Bank ATM for daily data and also seasonal ARIMA for Monthly data.

All the best

Thank you Julian Vasilev for putting your views, I hope this is going to be very useful for me in my work.

Thank you Ahammad Hossain, It would be helpful if you include the reasons or views behind using mentioned models.

Demand Depends on many factors: For example.

1. Location

2. Urbanization

3. habits of local people

4. Nearest ATM ran out of cash

5. Festivals

6. Early days in a month

7. End days in a month.

8. If government announces any freebie for a selected days of payment

9. Type of ATM. Both cash receipt and draw.

10. parking facility near the ATM.

11. Commercial complexes nearby

12. In housed in an establishment

13. Availability of denomination. whether ATM dispense 100,500,1000 or only 500, 1000 notes

14. ambiance nearby

15. Dispensing New or Old currency

16. Customer service charges collected

17. Receipt printed or not

18. Single or multiple ATMs exist in same room

19. Ground Floor/First Floor or higher floors

20. Average Queue in front of ATM.

We need to study all these factors to reach a optimal result.

 Thank you Lakshminarayana Sadasivuni, Mentioned factors are really very important in my work.

i think you should try ARIMA too

I think ARFIMA

 Dear I think you should go for ARFIMA

If you know how to use R, Rob Hyndman's package 'forecast' is able to compare a number of different techniques in one pass. As I am lazy I like to start there and see how it performs before going too far into the weeds.  see http://robjhyndman.com/seminars/melbournerug/

 Hi,

first i agree with Julian;

second i would like to propose more details. for example:

1- you may want to compose new 7 time series (TSMon, TSTue,TSWed,..,TSSun). one time series for each day in the week. i want to point that based on my personal experience with ARIMA, such method can overcome the existence of seasonality better than it's done via ARIMA. 

next you may apply ARIMA for each time series. i would expect to have better accuracy using this approach (well, it works well in my research).

the same can be done for each day in the month. in other words, we compose 31 time series. each time series will represent a day in the month.

2- although Neural Network works fine in many cases, but i strongly suggest that you try other models. for instance, if you decide to compose a 7 time series as i suggested in point 1, then you may apply other technique as well such as Genetic Programming (where the dependent variable is the current day and the explanatory variables are the previous 6 days). bear in mind that Neural Network is a black box.   

or long term prediction, you may like to use PSF:

https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm

and seasonalPSF:

https://www.researchgate.net/publication/304580701_Introduction_of_seasonality_concept_in_PSF_algorithm_to_improve_univariate_time_series_predictions

Article PSF: Introduction to R Package for Pattern Sequence Based Fo...

Article Introduction of seasonality concept in PSF algorithm to impr...

It is a simple case of Time series analysis based on past data.

Following