sutte: Forecasting Data using Alpha-Sutte Indicator

In sutteForecastR: Forecasting Data using Alpha-Sutte Indicator

Description

The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data.

Usage

alpha.sutte(x)

Arguments

x	a time series data

Author(s)

Ansari Saleh Ahmar <[e-mail]>

References

Ahmar, A.S. (2017) <doi:10.17605/osf.io/rknsv>

Ahmar, A. S., Rahman, A., & Mulbar, U. (2017) <doi:10.17605/osf.io/n68yq>

Examples

x <- c(94.77, 96.23, 98.12, 99.09, 100.04, 100.12, 99.93, 100.09,
101.44, 102.38, 103.68,104.12, 104.81, 105.35, 106.36, 106.89,107.35,
107.21, 107.72, 108.54, 109.57, 112.03)

alpha.sutte(x)

Example output

$Tes_Data
[1] 106.89 107.35 107.21 107.72 108.54 109.57 112.03

$Forecast_AlphaSutte
[1] 107.1095 107.5858 108.0190 107.4941 107.9974 108.9381 110.3597

$Forecast_AutoARIMA
   Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
16       107.1879 106.4417 107.9340 106.0467 108.3290
17       108.0157 106.9605 109.0709 106.4019 109.6295
18       108.8436 107.5512 110.1359 106.8671 110.8200
19       109.6714 108.1792 111.1637 107.3892 111.9537
20       110.4993 108.8309 112.1677 107.9477 113.0509
21       111.3271 109.4995 113.1548 108.5320 114.1223
22       112.1550 110.1809 114.1291 109.1359 115.1741

$Forecast_HoltWinters
   Point Forecast    Lo 80    Hi 80     Lo 95    Hi 95
16       107.2981 106.5136 108.0825 106.09834 108.4978
17       108.2694 106.6180 109.9208 105.74381 110.7950
18       109.2407 106.5060 111.9754 105.05840 113.4230
19       110.2120 106.2214 114.2026 104.10887 116.3152
20       111.1833 105.7867 116.5799 102.92996 119.4367
21       112.1547 105.2172 119.0921 101.54472 122.7646
22       113.1260 104.5238 121.7281  99.97015 126.2818

$Forecast_NNETAR
   Point Forecast
16       107.1564
17       107.9264
18       108.6376
19       109.2614
20       109.7802
21       110.1906
22       110.5013

$Forecast_Robust_exponential_smoothing
   Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
16       107.0758 106.1224 108.0292 105.6177 108.5340
17       107.7773 106.0558 109.4987 105.1445 110.4100
18       108.4645 106.0649 110.8641 104.7947 112.1343
19       109.1379 106.0735 112.2023 104.4513 113.8244
20       109.7976 106.0621 113.5331 104.0847 115.5106
21       110.4441 106.0240 114.8642 103.6841 117.2040
22       111.0774 105.9564 116.1984 103.2455 118.9093

$Forecast_Theta
   Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
16       106.7285 105.3988 108.0581 104.6949 108.7620
17       107.0970 105.2166 108.9773 104.2212 109.9727
18       107.4655 105.1626 109.7684 103.9435 110.9875
19       107.8340 105.1749 110.4932 103.7672 111.9008
20       108.2025 105.2295 111.1755 103.6557 112.7494
21       108.5710 105.3143 111.8278 103.5903 113.5518
22       108.9396 105.4219 112.4573 103.5597 114.3194

$AutoARIMA
Series: al_mi_10 
ARIMA(0,1,0) with drift 

Coefficients:
       drift
      0.8279
s.e.  0.1498

sigma^2 estimated as 0.339:  log likelihood=-11.76
AIC=27.52   AICc=28.61   BIC=28.8

$HoltWinters
Holt-Winters exponential smoothing with trend and without seasonal component.

Call:
HoltWinters(x = al_mi_10, gamma = FALSE)

Smoothing parameters:
 alpha: 0.9262261
 beta : 1
 gamma: FALSE

Coefficients:
         [,1]
a 106.3267473
b   0.9713172

$NNETAR
Series: al_mi_10 
Model:  NNAR(1,1) 
Call:   nnetar(y = al_mi_10)

Average of 20 networks, each of which is
a 1-1-1 network with 4 weights
options were - linear output units 

sigma^2 estimated as 0.2146

$Robust_exponential_smoothing
ROBETS(M,Ad,N) 

Call:
 robets(y = al_mi_10) 

  Smoothing parameters:
    alpha = 0.9997 
    beta  = 0.2574 
    phi   = 0.9798 

  Initial states:
    sigma = 0.0073 
    l = 96.135 
    b = 0.6075 

  sigma:  0.0088

  robAIC  robAICc   robBIC 
34.81830 37.00012 36.94246 

$Theta_Model
Theta 

Call:
 forecast::ets(y = y, model = "ANN", opt.crit = "mse") 

  Smoothing parameters:
    alpha = 0.9999 

  Initial states:
    l = 94.7702 

  sigma:  1.0376

     AIC     AICc      BIC 
45.58019 47.76201 47.70434 

sutteForecastR documentation built on May 1, 2019, 7:42 p.m.