R: Feature-Based Forecast Model Selection

# Feature-Based Forecast Model Selection

## Documentation for package ‘seer’ version 1.1.8

- DESCRIPTION file
- Package README

## Help Pages

accuracy_arima | Calculate accuracy measue based on ARIMA models
accuracy_ets | Forecast-accuracy calculation
accuracy_mstl | Calculate accuracy based on MSTL
accuracy_nn | Calculate accuracy measure calculated based on neural network forecasts
accuracy_rw | Calculate accuracy measure based on random walk models
accuracy_rwd | Calculate accuracy measure based on random walk with drift
accuracy_snaive | Calculate accuracy measure based on snaive method
accuracy_stlar | Calculate accuracy measure based on STL-AR method
accuracy_tbats | Calculate accuracy measure based on TBATS
accuracy_theta | Calculate accuracy measure based on Theta method
accuracy_wn | Calculate accuracy measure based on white noise process
acf5 | Autocorrelation-based features
acf_seasonalDiff | Autocorrelation coefficients based on seasonally differenced series
build_rf | build random forest classifier
cal_features | Calculate features for new time series instances
cal_m4measures | Mean of MASE and sMAPE
cal_MASE | Mean Absolute Scaled Error(MASE)
cal_medianscaled | scale MASE and sMAPE by median
cal_sMAPE | symmetric Mean Absolute Pecentage Error(sMAPE)
cal_WA | Weighted Average
classify_labels | Classify labels according to the FFORMS famework
classlabel | identify the best forecasting method
combination_forecast_inside | This function is call to be inside fforms_combination
convert_msts | Convert multiple frequency time series into msts object
e_acf1 | Autocorrelation coefficient at lag 1 of the residuals
fcast_accuracy | calculate forecast accuracy from different forecasting methods
fforms_combinationforecast | Combination forecast based on fforms
fforms_ensemble | Function to identify models to compute combination forecast using FFORMS algorithm
holtWinter_parameters | Parameter estimates of Holt-Winters seasonal method
prepare_trainingset | preparation of training set
rf_forecast | function to calculate point forecast, 95% confidence intervals, forecast-accuracy for new series
sim_arimabased | Simulate time series based on ARIMA models
sim_etsbased | Simulate time series based on ETS models
sim_mstlbased | Simulate time series based on multiple seasonal decomposition
split_names | split the names of ARIMA and ETS models
stlar | STL-AR method
unitroot | Unit root test statistics