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What's the statistical strategy to compute 2 month's ahead sales forecasts? I'm trying to build a chart for some software I'm creating.

Piecemealing this together with a lot of thought, it appears that I'm faced with:

Seasonal Delta (SD) Sales often tend to be seasonal, no matter the business. If sales went up or down in Q4 last year, they often, but not always, will mimic this pattern in Q4 this year.

Large Sample Delta (LSD) There's already often, but not always, a natural rate of growth or decline occurring from now and 12 months prior to now, and one can carry that out too. That then becomes a factor.

Recent Sample Delta (RSD) There's already often, but not always, a natural rate of growth or decline occurring from now and the prior month, and one can carry that out too. That then becomes a factor.

Therefore, I guess if I average the top 3 delta's together, I get a Best Guess Delta (BSD) that I can apply to this month's average to get next month's average. Do it one more time and get the month following that. So, that's a 2 month forecast.

Is that the statistical strategy to do this? Or, should I weight things? For instance, wouldn't RSD have more weight in probability than LSD and SD? And wouldn't LSD have the least weight in probability?

Please note -- my math training stopped right before Calculus. So, Algebra, Trig, Geometry -- these are things I know. I'm also a PHP programmer, if that means anything.