we need to Explore the data distributions for ATMs data to improve the operational efficiency of ATMs
No. of transactions per day
Amount withdrawn per day
No. of Customers using an ATM
Identify potential Customers for cross selling
Amount of transactions by each Customer
Identify areas to launch new products or marketing campaign.
Amount of transactions in an area
and you can use multiple time series for forecasting for different sites
you can drop outlier and proceed with evaluating the assumption of time series and then build a forecasting model
Multiple Time Series
We can plot multiple time series in one chart by combining both the series into a matrix.
Get the data points in form of a R vector.
rainfall1 <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071)
rainfall2 <- c(655,1306.9,1323.4,1172.2,562.2,824,822.4,1265.5,799.6,1105.6,1106.7,1337.8)
Convert them to a matrix.
combined.rainfall <- matrix(c(rainfall1,rainfall2),nrow = 12)
Convert it to a time series object.
rainfall.timeseries <- ts(combined.rainfall,start = c(2012,1),frequency = 12)
Print the timeseries data.
Plot a graph of the time series.
plot(rainfall.timeseries, main = “Multiple Time Series”)