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forecasting.R
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## Function for a creation of models of independent variables
creation_models_arima <- function(tbl,
ts_vars,
spline = F,
sport = 1,
a = .05,
...) {
if (is.list(ts_vars)) {
map_args <- ts_vars[[sport_list[id == sport, name]]]
} else {
map_args <- ts_vars
}
## Creation ARIMA models for independent variables
main_arima <- map(map_args[map_args %in% colnames(tbl)], \(i) {
ts <- tbl[[i]]
auto.arima(ts,
allowdrift = T,
allowmean = T,
test.args = list(a = a),
seasonal.test.args = list(a = a,
max.D = 2),
...) |> suppressWarnings()
})
names(main_arima) <- map_args[map_args %in% colnames(tbl)]
return(main_arima)
gc(reset = T, full = T)
}
## Function for a creation of forecasting values of independent variables
forecasting_models_arima <- function(tbl,
ts_vars,
models,
sport = 1,
h = 15,
...) {
if (is.list(ts_vars)) {
map_args <- ts_vars[[sport_list[id == sport, name]]]
} else {
map_args <- ts_vars
}
## Creation table of forecasting values for independent variables
### 1) lower boarder of confidence interval
lower_values <- as.data.table(
map(map_args[map_args %in% colnames(tbl)], \(i) {
f <- forecast(models[[i]],
h = h,
...)
if (any(as.numeric(f$lower[, 1]) <= 0 | is.na(as.numeric(f$lower[, 1])))) {
0
} else {
if (i == 'expected_goals') {
round(f$lower[, 1], 2)
} else {
round(f$lower[, 1], 0)
}
}
})
)
### 2) mean of confidence interval
mean_values <- as.data.table(
map(map_args[map_args %in% colnames(tbl)], \(i) {
f <- forecast(models[[i]],
h = h,
...)
if (any(as.numeric(f$mean) <= 0 | is.na(as.numeric(f$mean)) )) {
0
} else {
if (i == 'expected_goals') {
round(f$mean, 2)
} else {
round(f$mean, 0)
}
}
})
)
### 3) upper boarder of confidence interval
upper_values <- as.data.table(
map(map_args[map_args %in% colnames(tbl)], \(i) {
f <- forecast(models[[i]],
h = h,
...)
if (any(as.numeric(f$upper[, 1]) <= 0 | is.na(as.numeric(f$upper[, 1])) )) {
0
} else {
if (i == 'expected_goals') {
round(f$upper[, 1], 2)
} else {
round(f$upper[, 1], 0)
}
}
})
)
colnames(lower_values) <- map_args[map_args %in% colnames(tbl)]
colnames(mean_values) <- map_args[map_args %in% colnames(tbl)]
colnames(upper_values) <- map_args[map_args %in% colnames(tbl)]
return(list('lower_values' = lower_values,
'mean_values' = mean_values,
'upper_values' = upper_values))
gc(reset = T, full = T)
}
## Function for a creation of models of independent variables
forecasting_models_rf <- function(tbl,
indep_vars,
dep_vars,
forecast_models,
side = 'home',
level_value = 1,
levels_factor = 2,
sport = 1,
...) {
if (is.list(dep_vars)) {
map_args <- dep_vars[[sport_list[id == sport, name]]]
} else {
map_args <- dep_vars
}
terms <- dependent_vars(tbl = tbl, indep_vars = indep_vars, dep_vars = dep_vars, sport = sport)
values <- c('lower_values', 'mean_values', 'upper_values')
predict_table <- as.data.table(
map(map_args, \(i) {
if (length(levels(as.factor(tbl[[i]]))) < levels_factor) tbl[[i]] <- as.factor(tbl[[i]])
rf_formula <- reformulate(termlabels = terms[[i]]$incidents, response = i, intercept = F)
dep_vars_for_predict <- randomForest(rf_formula, data = tbl, ...) |> suppressWarnings()
if (i == 'expected_goals') {
round( predict(dep_vars_for_predict, forecast_models[[values[level_value]]]), 2)
} else {
if (is.factor(tbl[[i]])) {
predict(dep_vars_for_predict, forecast_models[[values[level_value]]])
} else {
floor( predict(dep_vars_for_predict, forecast_models[[values[level_value]]]) )
}
}
} )
) %>% rename_all(~ map_args)
if (side == 'home') {
cbind(forecast_models[[values[level_value]]], predict_table) %>%
replace(. < 0, 0) %>%
select(-away_score_full) %>%
suppressWarnings()
} else {
cbind(forecast_models[[values[level_value]]], predict_table) %>%
replace(. < 0, 0) %>%
select(-home_score_full) %>%
suppressWarnings()
}
}