In a Time Series Problem, is it possible to forecast quantities by learning the patterns of other items? What are my options?

Suppose I own a store that sells a variety of apples and I have the following stats each month.

• Report Date
• Type of Apple (TA)
• Quantity Available(QA)
• Quantity Sold in the Past 30 days(QS30)
• Quantity Shipping In (QSI)
• Quantity Needed to Order(QN)

Lets make the following assumptions/givens:

• There are three types of apples: red apples, green apples and yellow apples.

• T(1) denotes the first month and T(60) denotes the 60th month.

• QA @ T(i + 1) = QA@T(i) + QSI@T(i) – QS30@T(i+1)

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I can provide all the data from T1-60 for each apple.

I can also provide all the data for T61 besides QN for each apple.

My goal is predict QN at T61, or the 61st month for each apple.

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If I am only concerned about Red Apples, I can just use ARIMAX with data pertaining to only red apples or VAR right?

But what if I suddenly introduce a new apple type such as orange apples and only have a history from T1-4?

Given I need to use T1-4 for the orange apples…
Is it possible to use other data from the red, green and yellow to aid in properly calculating a QN value for orange apples at T5?