Transport.TS
Transport Trend Analysis 
Quick Links
Overview
This project leverages the IATA and ICAO Codes API to develop an interactive R Shiny web application. The app enables users to search, analyze, and visualize global airport and airline data. By integrating API-driven data retrieval with advanced data manipulation and visualization techniques, the project provides an intuitive platform for exploring global airport information, featuring dynamic charts and interactive visuals to enhance user experience.
Installation
Use the below API to be able to download the endpoints into your desired dataframe using Rapid API
library(httr)
library(jsonlite)
# Function to get data from the API
get_airport_data <- function(query, api_key) {
url <- paste0("https://vacationist.p.rapidapi.com/", query)
response <- GET(url, add_headers(
"X-RapidAPI-Key" = api_key,
"X-RapidAPI-Host" = "vacationist.p.rapidapi.com"
))
content <- content(response, as = "text")
data <- fromJSON(content, flatten = TRUE)
return(data)
}
# Example: Fetching airports
api_key <- "YOUR_API_KEY"
airport_data <- get_airport_data("airports", api_key)
print(airport_data)
Usage
Plotting time series data
library(TSstudio)
data(USgas)
# Ploting time series object
ts_plot(USgas,
title = "US Monthly Natural Gas Consumption",
Ytitle = "Billion Cubic Feet")
Seasonality analysis
# Seasonal plot
ts_seasonal(USgas, type = "all")
# Heatmap plot
ts_heatmap(USgas)
Correlation analysis
# ACF and PACF plots
ts_cor(USgas, lag.max = 60)
# Lags plot
ts_lags(USgas, lags = 1:12)
# Seasonal lags plot
ts_lags(USgas, lags = c(12, 24, 36, 48))
Training forecasting models
# Forecasting applications
# Setting training and testing partitions
USgas_s <- ts_split(ts.obj = USgas, sample.out = 12)
train <- USgas_s$train
test <- USgas_s$test
# Forecasting with auto.arima
library(forecast)
md <- auto.arima(train)
fc <- forecast(md, h = 12)
# Plotting actual vs. fitted and forecasted
test_forecast(actual = USgas, forecast.obj = fc, test = test)
# Plotting the forecast
plot_forecast(fc)
# Run horse race between multiple models
methods <- list(ets1 = list(method = "ets",
method_arg = list(opt.crit = "lik"),
notes = "ETS model with opt.crit = lik"),
ets2 = list(method = "ets",
method_arg = list(opt.crit = "amse"),
notes = "ETS model with opt.crit = amse"),
arima1 = list(method = "arima",
method_arg = list(order = c(2,1,0)),
notes = "ARIMA(2,1,0)"),
arima2 = list(method = "arima",
method_arg = list(order = c(2,1,2),
seasonal = list(order = c(1,1,1))),
notes = "SARIMA(2,1,2)(1,1,1)"),
hw = list(method = "HoltWinters",
method_arg = NULL,
notes = "HoltWinters Model"),
tslm = list(method = "tslm",
method_arg = list(formula = input ~ trend + season),
notes = "tslm model with trend and seasonal components"))
# Training the models with backtesting
md <- train_model(input = USgas,
methods = methods,
train_method = list(partitions = 6,
sample.out = 12,
space = 3),
horizon = 12,
error = "MAPE")
# A tibble: 6 x 7
model_id model notes avg_mape avg_rmse `avg_coverage_80%` `avg_coverage_95%`
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 arima2 arima SARIMA(2,1,2)(1,1,1) 0.0557 167. 0.583 0.806
2 hw HoltWinters HoltWinters Model 0.0563 163. 0.736 0.889
3 ets1 ets ETS model with opt.crit = lik 0.0611 172. 0.681 0.903
4 ets2 ets ETS model with opt.crit = amse 0.0666 186. 0.458 0.833
5 tslm tslm tslm model with trend and seasonal components 0.0767 220. 0.417 0.667
6 arima1 arima ARIMA(2,1,0) 0.188 598. 0.875 0.958
# Plot the performance of the different models on the testing partitions
plot_model(md)
# Holt-Winters tunning parameters with grid search
hw_grid <- ts_grid(USgas,
model = "HoltWinters",
periods = 6,
window_space = 6,
window_test = 12,
hyper_params = list(alpha = seq(0,1,0.1),
beta = seq(0,1,0.1),
gamma = seq(0,1,0.1)))
plot_grid(hw_grid, type = "3D")
