How to calculate general trendline for a time series

  • Thread starter ateixeira
  • Start date
  • #1
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Hi there,

Given a time series with data points x_1, x_2, x_3,...,x_n I want to be able to extrapolate its future behaviour. I can see three options:

  1. \sum_i (x_(i+1)-x_i)/x_i
  2. count of how many terms of (x_(i+1)-x_i) are positive and negative
  3. Assume linearity and calculate m for the best fit linear aproximation

What I want to know is:
  1. Do you think that this makes sense?
  2. Do you know of any standardized way to solve this?

Thanks
 

Answers and Replies

  • #2
Stephen Tashi
Science Advisor
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[*]Do you think that this makes sense?

No, it doesn't make sense to expect that an answer that can be mathematically justified when so little information about the time series has been given.

[*]Do you know of any standardized way to solve this?

You can find many references on "time series analysis". They won't solve your problem as you have stated it since the statement doesn't give enough information. However, if you look up material on time series analysis it will show you the type of assumptions or facts that must be given in order for various prediction methods to work.
 
  • #3
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Thanks for your reply Stephen and sorry for not being specific enough.

No, it doesn't make sense to expect that an answer that can be mathematically justified when so little information about the time series has been given.
In that case let us assume that I'm analyzing the sales of a given product on given time frame (say 6 months).

What I want to be able to do is:
  1. understand if the sales are experiencing an upward or downward tendency
  2. Predict the next data point
 
  • #4
Stephen Tashi
Science Advisor
7,707
1,515
In that case let us assume that I'm analyzing the sales of a given product on given time frame (say 6 months).

That is still not enough mathematical information about the problem. For example, the sales of products like sun tan lotion may depend on the season or the weather. If you don't understand mathematical modeling, I suggest that you just do a linear regression (i.e. a linear curve fit). Or post a plot of the data and I'm sure someone will chime-in with an opinion about what kind of curve fits it.
 

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