I have two separate problems, but both suffer from a paucity of data problems:
- logistic regression
- Time series prediction
For logistic regression, I have a tiny dataset with 10 observations which have variables such as:
car_purchase_status(outcome flag with Yes/No values).
Now I have a new 11th customer with variables such as
income. Now I would like to know whether this 11th customer will buy a car or not.
Should I spend resources to influence him to buy a car? Am I spending my resources on the right customer? For example: Is there any way that I can find out that the 11th customer has 70% or 80% pc chance of buying a car?
So spending some marketing efforts such as calls can help us convince him to buy a car (100%). So, how can I do this? Any advice, please? Should I just give up straight away that this problem is impossible to solve with such low data, or are there any simple statistical techniques that can help me gain some insights about the 11th customer?
For Time series prediction, I have only 10 observations, each spaced at 20 days gap. For ex: I have their
Now, with the given 10 observations, I would like to predict the
day500, day321, day621etc.
So, is it possible to run time series forecasting with such a short series? Can you guys guide me on this, please? Here, also, should I give up because of low data points, or are there any methods that I can use to predict the future timestamp points based on short input time series?
Can you guys please help me with the list of steps/topics that can help me do this?