- #1
kdbnlin78
- 34
- 0
Hey all.
I have some data, approximately 6 months worth. It is values that do not depend on time but are represented by a pair (x, y) such that the values x is measured at a point in time y. Therefore the data is equivalent to measuring how much stock I have in a given time period, say. The measurements occur once a day for 6 months.
I need to take this data and create a model that would allow me to predict values of x in (say) one month, three months and six months time.
My question is what is the best method to use? Should I suupose that a power law exists and us a regression model, or would a simple plotting of moving averages allow me to make an accurate prediction?
I guess I don't necessarily need to end with a function model representing x ans a function of y (x = f(y)) but more that I can use some statistical inference to accurately forecast the values of x_{i} in the future.
Any help on this matter would be very appreciated.
Regards,
kdbnlin
I have some data, approximately 6 months worth. It is values that do not depend on time but are represented by a pair (x, y) such that the values x is measured at a point in time y. Therefore the data is equivalent to measuring how much stock I have in a given time period, say. The measurements occur once a day for 6 months.
I need to take this data and create a model that would allow me to predict values of x in (say) one month, three months and six months time.
My question is what is the best method to use? Should I suupose that a power law exists and us a regression model, or would a simple plotting of moving averages allow me to make an accurate prediction?
I guess I don't necessarily need to end with a function model representing x ans a function of y (x = f(y)) but more that I can use some statistical inference to accurately forecast the values of x_{i} in the future.
Any help on this matter would be very appreciated.
Regards,
kdbnlin