Doubts Regarding Sales Forecast Model

I have few more clarification regarding time series.
Firstly
In my model the month level product sales value represented across 1st day of every month. So that the key time column is of datetime datatype containing a sequence of dates representing the 1st day of every month of the year.
Eg: 2006-01-01, 2006-02-01€¦€¦€¦. etc. all in (yy-mm-dd) format
But
when I make prediction for next five months, though it makes monthly
predictions the date part for the months are random whereas I expect
the date part to be 1st of every month. What is the reason for this and how can I overcome it.
Secondly
Predicted
sales values for some time period are negative though I do not have any
negative value in the training data. What is the reason for this and
how can I rectify it?

Thirdly
In
one of your earlier posts you had said that the time series algorithm
does not have any built in time intelligence but uses the key time
column as a time sequence stamp. So If have to make predictions for a
particular time period where the time slice for each time period is 25
days or 50 days etc, then I understand that the input data used to
train the model should be in the same time sequence.
Or
Can I specify the span of the time period according to which the prediction needs to be made?
Basically
how can I use the same time series model to make monthly, yearly,
quarterly, daily or predictions or for custom time period like I have
mentioned above.