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 1^st day of every month.So that the key time column is of datetime datatype containing a sequence of dates representing the 1^st 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 1^st 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.

1: There really isn't a workaround - you should use your own logic for dates

2: There is a parameter MINIMUM_PREDICTION_VALUE (or something like that) that you can set to 0, and you won't get negative predictions.

3: You should make unique models for each period you want to analyze - the patterns and periodicities are different, and need different analyses.

|||Thanks Jamie