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- Series: y
- ARIMA(0,0,0) with zero mean
- Coefficients:
- dowSunday dowMonday dowTuesday dowWednesday dowThursday dowFriday dowSaturday daypartM daypartA
- 0.1427 0.1425 0.0912 0.0312 0.0136 0.1195 0.0841 0.1051 0.1275
- s.e. 0.1399 0.2124 0.2120 0.1528 0.2013 0.2357 0.2375 0.1285 0.0451
- daypartE daypartLN inv_last24 regionSouth regionNorth Central
- 0.1697 0.0334 0.0240 -0.0234 -0.100
- s.e. 0.0968 0.0736 0.1085 0.0464 0.049
- sigma^2 estimated as 0.007612: log likelihood=31.9
- AIC=-33.8 AICc=206.2 BIC=-20.44
- Training set error measures:
- ME RMSE MAE MPE MAPE MASE ACF1
- Training set -6.321953e-17 0.04112732 0.03184536 -Inf Inf 0.3702166 0.2447627
- library(forecast)
- ARIMA000 <- rep(10,10)
- FirstARIMA <- ts(ARIMA000)
- noise <- rnorm(10, mean = 0, sd = 1)
- SecondARIMA <- ts(ARIMA000 + noise)
- auto.arima(FirstARIMA)
- Series: FirstARIMA
- ARIMA(0,0,0) with non-zero mean
- Coefficients:
- intercept
- 10
- sigma^2 estimated as 0: log likelihood=Inf
- AIC=-Inf AICc=-Inf BIC=-Inf
- auto.arima(SecondARIMA)
- Series: SecondARIMA
- ARIMA(0,0,0) with non-zero mean
- Coefficients:
- intercept
- 10.1683
- s.e. 0.2434
- sigma^2 estimated as 0.6581: log likelihood=-11.57
- AIC=27.14 AICc=28.86 BIC=27.75
- plot.ts(FirstARIMA)
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