HI Neel,

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Amount withdrawn per day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.

print(rainfall.timeseries)

# Plot a graph of the time series.

plot(rainfall.timeseries, main = â€śMultiple Time Seriesâ€ť)

Regards,

Tony