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Let me make it clear by make an example:

Suppose I knew a person's cost each month for 3 years like:

2016Jan : $2500

2016Feb : $4000

2016Mar : $3500
...

Just according to this, can I predict how much he earned each month in these years? If I could, what model should use? If I couldn't, what info I need to make this possible. If I couldn't guess in 100 percent but I could guess by some probability, what model should I use?

Another example: Need to estimate how much each store sells in each city, however I could only got the sales when they record it in electric system, if they didn't record it in electric system(may use paper or something else); then I don't know, how can I get all sales, not just the sales record in electric system?

Any suggestion is welcomed

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Actually you can use almost any regression model. When you do not want to go too much into theory simply use a gaussian process provided by sklearn. This model gives you predictions and uncertainties.

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  • $\begingroup$ Thanks for let me know the gaussian process by sklearn $\endgroup$ – cloudscomputes Nov 14 '18 at 9:09
  • $\begingroup$ According to my understanding these days, GP need a prior (x,y), to build on (x*,y*), however, in my case, I don't have any y, what I have is only x and x*, any further ideas? $\endgroup$ – cloudscomputes Nov 16 '18 at 8:40
  • $\begingroup$ You can use the month (jan., feb., etc..) as your $x$ and the income as your $y$. Afterwards you calculate for missing $x$ your corresponding $y$ with the gp. $\endgroup$ – Andreas Look Nov 16 '18 at 8:49
  • $\begingroup$ I don't have any income data, I only have person's living cost $\endgroup$ – cloudscomputes Nov 19 '18 at 3:07

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