Open driscoll42 opened 3 years ago
From discussions on Discords:
R^2 needs a model to give scores VAR model If you are doing pure predictive analysis (the goal is to forecast 2-3 days forward), VAR might be suitable
"ETH price is x% correlated with GPU price" https://en.wikipedia.org/wiki/Granger_causality
Then you should include that into the analysis (all data going back from launch) And if you want to use R^2, you need to fit an actual model. R^2 is for residuals so you have two model y=f(x) and y=g(x) their residuals are r1 = y - f(x) and r2 = y - g(x) and the test just compares them - rSquared = 1 - sum(r12) / sum(r22) where r1*2 is applied element-wise so you would fit, as an example, changeInPriceOfGpus = a changeInPriceOfETH + b that's one model and another one is just changeInPriceOfGpus = c where a, b, c are adjustable parameters and then you can use R^2 to compare the former to the latter
plot the difference in prices so take p(t + Δt) / p(t) for both time series and plot one on X axis and one on Y but I want you to plot Y = ln(pGPU(t + Δt) / pGPU(t)) against X = ln(pETH(t + Δt) / pETH(t))
So, the closer to a y=x line, would mean that a change in one implies the change in the other? Or rather they're correlated Big Bux Chungus — Today at 5:47 PM Yes, in a way. Not necessarily y=x y=a*x+b
mining profitability depends on the price, your hashrate, and the network hashrate as price your_hash_rate / total_network_hash_rate block_payment / blocks_per_second * time_in_seconds block_payment, blocks_per_second don't depend on time total_network_hash_rate changes over time https://www.coinwarz.com/mining/ethereum/hashrate-chart Ethereum Hashrate Chart that plot is just an estimate, so you might as well just draw a straight line
or at least aggregate over a sufficiently large window a better GPU results in a better hashrate you also pay for electricity, so you pay
Suggestion from OMSA slack: Even ACF/PACF plots would be really interesting to see autocorrelation within series, and if you can look at lags between series (e.g., prices of a GPU type jump one week after ETH jumps) it would make the analysis quite actionable
They're covered in the Time Series class, but totally understand if you haven't taken that. The concepts are pretty straight forward - they're measures of the autocorrelation between various points in a time series. So, you might find that what happens tomorrow is highly correlated to what happens today, what happens next week is somewhat correlated to what happens today, and what happens next month is completely uncorrelated. In R there's a function in the stats package - https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/acf A helpful overview - https://online.stat.psu.edu/stat462/node/188/#:~:text=Autocorrelation%20and%20Partial%20Autocorrelation,%2C2%2C....&text=This%20value%20of%20k%20is,and%20is%20called%20the%20lag.
Describe the solution you'd like There's a relationship between the GPU pricing and Cryptocurrency. It would be interesting to explore this further.