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Methods for forecasting price?
It's not impossible to forecast the future price of a commodity. However, it's exactly that; an educated guess, much like the weather, and the further out that prediction is made, the higher the percentage error is expected. A lot of information is gathered by various instruments, spotters etc at a very high cost of time and money, to produce a prediction that starts breaking down after about five days and is no more than a wild guess after about ten. How accurately a price can be forecast depends on the commodity. There are seasonal and thus cyclical changes in many commodities, on top of which there is a general trend which is nearer term. A pretty decent prediction can thus be arrived at with a relatively simple seasonally-adjusted percentage change algorithm; take a moving average of the last few measurements, compute the percent change versus the same period last year (current minus last divided by last) and multiply it by last year's number for the current day or month to arrive at a pretty decent prediction for the current and near-future periods (up to about as far ahead as you have looked behind). Another thing you may need to do is normalize. Many price graphs are very jittery; the price of a stock may fluctuate many percentage points on a single day, and there's a lot of "noise" inherent in them. A common tool to normalize is a box-and-whisker plot, which for a given time period will aggregate all samples within that period, and give you a measurement of the lowest sample, highest sample, median, and quartiles (the range of each 25% of the full sample space). Box plots can also be plotted on the "interquartile range" or "middle fifty"; this throws away the very noisy outliers and constructs a much more regular plot from the inner part of the bell curve. You can reverse-engineer a best-fit line connecting the elements of each box, and the closer two lines are, the more likely the real future data will be around that area (because the quartile between those to lines is very dense; 25% of the values are in a very small range meaning many samples occurred there). Lastly, there are outside factors that are not included in simple percentage growth. Big news must be taken into account by introducing more subjective guesses about future data. If you see an active hurricane season coming (or a hurricane bearing down on Galveston/Houston) then it's reasonable to assume that the price of oil and/or refined oil products (like gas and jet fuel) will skyrocket. A cyclical growth model will not predict these events, but you can factor in the likelihood of a big change with a base onto which you add last year's numbers, and onto that you add regular growth. Conversely, when a huge spike happens due to a non-cyclical event like a natural disaster, you must smooth it out by reducing the readings to fit in the curve, otherwise your model for next year will expect the same anomaly at the same time and so it will be wrong. These adjustments are necessary, but the more of them you make, the less the graph reflects real history and the more it reflects what you think it should have been.