Residual sum of squares - y = response variable(dependent x...

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y = response variable (dependent) x = explanatory variable (independent) RSS = Residual sum of squares ~ sum of squared errors Multiple R squared measures how close your predicted value is with respect to the true value AIC = n log(SSE/n) + 2(K+2) Time series can be decomposed into: Trend Seasonal Variation Noise ---------- Exam October 15th One cheat sheet Multiplicative decomposition x(t) = s(t)T(t)E(t) Just the same as the classical decomposition when you do the log transformation s.window groups the indexes s.window = 5 , looking at 5 data points at a time standard is infinity, means that there is just one seasonal pattern for all the data For time.series, the columns equal: 1 seasonal 2 trend 3 error look up help file of stl s.window → if you don’t assign it, you are assuming it has the same pattern along the whole
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Unformatted text preview: time horizon t.window → what window size you want, 15 day moving average would be t.window = 15 Doing the moving average of the moving average lets you do an even number of moving avgs To figure out which moving average is best, compute the sum of the square of errors summation(Ei^2) Ei = Yi - estimated Yi Ways of forecasting We have y1, y2, y3, and the forecast Naive → forecast = y3 (the previous one is the only one that matters) average → forecast = (y1+y2+y3)/3 (all weighted the same) exponential → forecast = w1y1 + w2y2 + w3y3 (all weighted differently) Gets the weight by figuring out the least sum of square errors When forecasting, figuring out the trend and the seasonal component, not the noise...
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  • Fall '13
  • Lin
  • Regression Analysis, residual sum, squared errors, seasonal variation, multiplicative decomposition, average → forecast

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