comb OLS Electricity • ahead

1 - Comb OLS

library(ForecastComb)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

data(electricity)

print(head(electricity))

##             arima      ets     nnet  dampedt     dotm Actual
## Jan 2007 36980.16 35692.31 37047.91 35540.66 36044.28  36420
## Feb 2007 33587.29 33708.15 34523.56 33962.34 33821.69  32901
## Mar 2007 36005.55 37366.20 36049.72 37317.91 37119.29  34595
## Apr 2007 30925.25 30550.24 30721.91 30356.77 30350.95  29665
## May 2007 30394.78 29167.64 29241.89 28766.40 28910.84  30154
## Jun 2007 28938.14 29004.18 29211.91 29006.25 28229.28  28607

(forecasting_methods <- colnames(electricity)[1:5])

## [1] "arima"   "ets"     "nnet"    "dampedt" "dotm"

train_obs <- electricity[1:84, "Actual"]
train_pred <- electricity[1:84, forecasting_methods]
test_obs <- electricity[85:123, "Actual"]
test_pred <- electricity[85:123, forecasting_methods]

data <- ForecastComb::foreccomb(train_obs, train_pred, test_obs, test_pred)

start <- proc.time()[3]
obj <- ahead::comb_OLS(data)

## Registered S3 methods overwritten by 'ahead':
##   method                      from        
##   plot.foreccomb_res          ForecastComb
##   predict.foreccomb_res       ForecastComb
##   print.foreccomb_res_summary ForecastComb
##   summary.foreccomb_res       ForecastComb

print(proc.time()[3] - start)

## elapsed 
##   1.023

print(class(obj))

## [1] "foreccomb_res" "comb_OLS"

print(summary(obj))

## 
## Summary of Forecast Combination 
## ------------------------------- 
## 
## Method:  Ordinary Least Squares Regression 
## 
## Individual Forecasts & Combination Weights: 
## 
##         Combination Weight
## arima           0.02152869
## ets            -0.20646266
## nnet            0.20992792
## dampedt        -1.04349858
## dotm            1.97991049
## 
## Intercept (Bias-Correction):  962.3229 
## 
## Accuracy of Combined Forecast: 
## 
##                         ME     RMSE      MAE         MPE     MAPE
## Training Set  3.031556e-13 888.1433 697.8645 -0.08262472 2.254470
## Test set     -4.007742e+01 671.5214 536.0331 -0.24705122 1.841961
## 
## Additional information can be extracted from the combination object: 
## For fitted values (training set):  obj$Fitted 
## For forecasts (test set):  obj$Forecasts_Test 
## See  str(obj)  for full list.

print(obj$Accuracy_Test)

##                 ME     RMSE      MAE        MPE     MAPE
## Test set -40.07742 671.5214 536.0331 -0.2470512 1.841961

print(obj$Weights)

## [1]  0.02152869 -0.20646266  0.20992792 -1.04349858  1.97991049

# check
print(mean(predict(obj, test_pred) - test_obs))

## [1] 40.07742

plot(obj)

2 - Comb Ridge

start <- proc.time()[3]
obj <- ahead::comb_Ridge(data)
print(proc.time()[3] - start)

## elapsed 
##   0.006

print(obj$Weights)

## [1]    95.81502  -582.80574   685.98452 -2745.54330  5530.43579

print(summary(obj))

## 
## Summary of Forecast Combination 
## ------------------------------- 
## 
## Method:  Ridge Regression Regression 
## 
## Individual Forecasts & Combination Weights: 
## 
##         Combination Weight
## arima             95.81502
## ets             -582.80574
## nnet             685.98452
## dampedt        -2745.54330
## dotm            5530.43579
## 
## Intercept (Bias-Correction):  30582.2 
## 
## Accuracy of Combined Forecast: 
## 
##                         ME     RMSE      MAE         MPE     MAPE
## Training Set -1.169375e-12 889.9171 695.5390 -0.08465847 2.241061
## Test set     -4.696850e+01 672.4224 532.7352 -0.26365505 1.827328
## 
## Additional information can be extracted from the combination object: 
## For fitted values (training set):  obj$Fitted 
## For forecasts (test set):  obj$Forecasts_Test 
## See  str(obj)  for full list.

print(class(obj))

## [1] "foreccomb_res" "comb_Ridge"

print(obj$Accuracy_Test)

##                ME     RMSE      MAE       MPE     MAPE
## Test set -46.9685 672.4224 532.7352 -0.263655 1.827328

# check 
print(mean(predict(obj, test_pred) - test_obs))

## [1] 46.9685

plot(obj)

3 - Comb GLMNET

start <- proc.time()[3]
obj <- ahead::comb_GLMNET(data)
print(proc.time()[3] - start)

## elapsed 
##   0.156

print(class(obj))

## [1] "foreccomb_res" "comb_GLMNET"

print(obj$Weights)

## [1] 0.02511533 0.00000000 0.26187911 0.00000000 0.66946352

print(summary(obj))

## 
## Summary of Forecast Combination 
## ------------------------------- 
## 
## Method:  GLMNET Regression Regression 
## 
## Individual Forecasts & Combination Weights: 
## 
##         Combination Weight
## arima           0.02511533
## ets             0.00000000
## nnet            0.26187911
## dampedt         0.00000000
## dotm            0.66946352
## 
## Intercept (Bias-Correction):  1117.331 
## 
## Accuracy of Combined Forecast: 
## 
##                        ME     RMSE      MAE         MPE     MAPE
## Training Set 3.551339e-12 950.6999 751.5739 0.007510127 2.417785
## Test set     9.697216e+01 707.1726 531.9225 0.342919879 1.796003
## 
## Additional information can be extracted from the combination object: 
## For fitted values (training set):  obj$Fitted 
## For forecasts (test set):  obj$Forecasts_Test 
## See  str(obj)  for full list.

print(obj$Accuracy_Test)

##                ME     RMSE      MAE       MPE     MAPE
## Test set 96.97216 707.1726 531.9225 0.3429199 1.796003

print(mean(predict(obj, test_pred) - test_obs))

## [1] 96.97216

plot(obj)