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Oct 22nd, 2017
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  1. Series: y
  2. ARIMA(0,0,0) with zero mean
  3.  
  4. Coefficients:
  5. dowSunday dowMonday dowTuesday dowWednesday dowThursday dowFriday dowSaturday daypartM daypartA
  6. 0.1427 0.1425 0.0912 0.0312 0.0136 0.1195 0.0841 0.1051 0.1275
  7. s.e. 0.1399 0.2124 0.2120 0.1528 0.2013 0.2357 0.2375 0.1285 0.0451
  8. daypartE daypartLN inv_last24 regionSouth regionNorth Central
  9. 0.1697 0.0334 0.0240 -0.0234 -0.100
  10. s.e. 0.0968 0.0736 0.1085 0.0464 0.049
  11.  
  12. sigma^2 estimated as 0.007612: log likelihood=31.9
  13. AIC=-33.8 AICc=206.2 BIC=-20.44
  14.  
  15. Training set error measures:
  16. ME RMSE MAE MPE MAPE MASE ACF1
  17. Training set -6.321953e-17 0.04112732 0.03184536 -Inf Inf 0.3702166 0.2447627
  18.  
  19. library(forecast)
  20.  
  21. ARIMA000 <- rep(10,10)
  22. FirstARIMA <- ts(ARIMA000)
  23. noise <- rnorm(10, mean = 0, sd = 1)
  24. SecondARIMA <- ts(ARIMA000 + noise)
  25.  
  26. auto.arima(FirstARIMA)
  27.  
  28. Series: FirstARIMA
  29. ARIMA(0,0,0) with non-zero mean
  30.  
  31. Coefficients:
  32. intercept
  33. 10
  34.  
  35. sigma^2 estimated as 0: log likelihood=Inf
  36. AIC=-Inf AICc=-Inf BIC=-Inf
  37.  
  38. auto.arima(SecondARIMA)
  39.  
  40. Series: SecondARIMA
  41. ARIMA(0,0,0) with non-zero mean
  42.  
  43. Coefficients:
  44. intercept
  45. 10.1683
  46. s.e. 0.2434
  47.  
  48. sigma^2 estimated as 0.6581: log likelihood=-11.57
  49. AIC=27.14 AICc=28.86 BIC=27.75
  50.  
  51. plot.ts(FirstARIMA)
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