Out-of-sample time series forecasting

Out-of-Sample time series forecasting is a common, important, and subtle task. The OOS package introduces a comprehensive and cohesive API for the out-of-sample forecasting workflow: data preparation, forecasting - including both traditional econometric time series models and modern machine learning techniques - forecast combination, model and error analysis, and forecast visualization.

See the OOS package website for examples and documentation.

Workflow and available Tools

1. Prepare Data

2. Forecast

3. Analyze

Model estimation flexibility and accessibility

Users may edit any model training routine through accessing a list of function arguments. For machine learning techniques, this entails editing caret arguments including: tuning grid, control grid, method, and accuracy metric. For univariate time series forecasting, this entails passing arguments to forecast package model functions. For imputing missing variables, this entails passing arguments to imputeTS package functions.

A brief example using an Arima model to forecast univariate time series:

# 1. create the central list of univariate model training arguments, univariate.forecast.training  
forecast_univariate.control_panel = instantiate.forecast_univariate.control_panel()  

# 2. select an item to edit, for example the Arima order to create an ARMA(1,1)   
	# view default model arguments (there are none)  
	forecast_univariate.control_panel$arguments[['Arima']] 
	# add our own function arguments  
	forecast_univariate.control_panel$arguments[['Arima']]$order = c(1,0,1) 

A brief example using the Random Forest to combine forecasts:

# 1. create the central list of ML training arguments 
forecast_combinations.control_panel = instantiate.forecast_combinations.control_panel()  

# 2. select an item to edit, for example the random forest tuning grid   
	# view default tuning grid  
	forecast_combinations.control_panel$tuning.grids[['RF']]  
	# edit tuning grid   
	forecast_combinations.control_panel$tuning.grids[['RF']] = expand.grid(mtry = c(1:6))  

Basic workflow

#----------------------------------------
### Forecasting Example
#----------------------------------------
# pull and prepare data from FRED
quantmod::getSymbols.FRED(
	c('UNRATE','INDPRO','GS10'), 
	env = globalenv())
Data = cbind(UNRATE, INDPRO, GS10)
Data = data.frame(Data, date = zoo::index(Data)) %>%
	dplyr::filter(lubridate::year(date) >= 1990)

# run univariate forecasts 
forecast.uni = 
	forecast_univariate(
		Data = dplyr::select(Data, date, UNRATE),
		forecast.dates = tail(Data$date,15), 
		method = c('naive','auto.arima', 'ets'),      
		horizon = 1,                         
		recursive = FALSE,

		# information set       
		rolling.window = NA,    
		freq = 'month',                   
		
		# outlier cleaning
		outlier.clean = FALSE,
		outlier.variables = NULL,
		outlier.bounds = c(0.05, 0.95),
		outlier.trim = FALSE,
		outlier.cross_section = FALSE,
		
		# impute missing
		impute.missing = FALSE,
		impute.method = 'kalman',
		impute.variables = NULL,
		impute.verbose = FALSE) 

# create multivariate forecasts
forecast.multi = 
	forecast_multivariate(
		Data = Data,           
		forecast.date = tail(Data$date,15),
		target = 'UNRATE',
		horizon = 1,
		method = c('ols','lasso','ridge','elastic','GBM'),

		# information set       
		rolling.window = NA,    
		freq = 'month',                   
		
		# outlier cleaning
		outlier.clean = FALSE,
		outlier.variables = NULL,
		outlier.bounds = c(0.05, 0.95),
		outlier.trim = FALSE,
		outlier.cross_section = FALSE,
		
		# impute missing
		impute.missing = FALSE,
		impute.method = 'kalman',
		impute.variables = NULL,
		impute.verbose = FALSE,
		
		# dimension reduction
		reduce.data = FALSE,
		reduce.variables = NULL,
		reduce.ncomp = NULL,
		reduce.standardize = TRUE) 

# combine forecasts and add in observed values
forecasts = 
	dplyr::bind_rows(
		forecast.uni,
		forecast.multi) %>%
	dplyr::left_join( 
		dplyr::select(Data, date, observed = UNRATE))

# forecast combinations 
forecast.combo = 
	forecast_combine(
		forecasts, 
		method = c('uniform','median','trimmed.mean',
				   'n.best','lasso','peLasso','RF'), 
		burn.in = 5, 
		n.max = 2)

# merge forecast combinations back into forecasts
forecasts = 
	forecasts %>%
	dplyr::bind_rows(forecast.combo)

# calculate forecast errors
forecast.error = forecast_accuracy(forecasts)

# view forecast errors from least to greatest 
#   (best forecast to worst forecast method)
forecast.error %>% 
	dplyr::mutate_at(vars(-model), round, 3) %>%
	dplyr::arrange(MSE)

# compare forecasts to the baseline (a random walk)
forecast_comparison(
	forecasts,
	baseline.forecast = 'naive',  
	test = 'ER',
	loss = 'MSE') %>% 
	arrange(error.ratio)

# chart forecasts
chart = 
	chart_forecast(
		forecasts,              
		Title = 'US Unemployment Rate',
		Ylab = 'Index',
		Freq = 'Monthly')

chart

Contact

If you should have questions, concerns, or wish to collaborate, please contact Tyler J. Pike