Sorry - I know this isn't strictly Python, and i'm more asking for a formula than code!
Usually Excel functions show how they are calculated but i can't find anything on this one!
So given a set of data, predict what the next value will be based on the previous values

Excel statistical functions: TREND

The TREND(known_y's, known_x's, new_x's, constant) function is used to perform Linear Regression. A least squares criterion is used and TREND tries to find the best fit under that criterion.

Is that what you are looking for?

not sure - Is that the definition from Excel?
So trend just uses the least squares criterion?

No. It uses linear regression which (waving my hands broadly) amounts to finding the 'best possible' line through a set of points, and, given N-1 coordinates for a new point, guessing that the other coordinate puts the point on that line. With error bars. Least squares is a good but not unique way to decide where that 'best possible' line lies.

┬┐Shouldn't this thread be in the Computer Science forum?

Ok thanks, would you rec recommend any other methods or do you think i should use least squares?
And yes it probably should be in Computer Science, sorry

Least squares has several things going for it:

  • It is very common, so there are many examples, code snippets, etc
  • It is well behaved
  • It is reasonably simple

Unless I had a reason to suppose something else would be better, I'd default to least squares fitting.

Warning: All these things work, if at all, only on 'good' data. Statistics and various curve fitting techniques are prime candidates for GIGO problems.

Thanks very much,
do you know how i'd find the best fit line in Python though? Is it easily writeable?
My variable are independent of each other though - i am trying to predict the mean temperature of a day given the previous days' mean temperature, so the day is irrelevant - it's only the previous temperatures i'm concerned with.