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The Covid19 pandemic had, and unfortunately still have, a significant impact on most of the major industries. While for some sectors, the impact was positive (such as online retails, internet and steaming providers, etc.), it was negative for others (such as transportation, tourism, entertainment, etc.). In both cases, we can leverage time series modeling to quantify the effect of the Covid19 on the sector.

One simplistic approach for quantifying the impact of the pandemic (whether it is positive or negative) would include the following steps:

• Split the series into pre-covid and post-covid
• Train a time series model with the pre-covid data. This would enable us to simulate the value of the series if there was no pandemic
• Using the trained model to forecast the horizon of the post-covid series
• Use the difference between the forecast and actual (post-covid series) to quantify the Covid19 impact on the series

To demonstrate this approach, I will use the sfo_passengers dataset from the sfo package. The sfo_passengers dataset provides monthly statistics about the San Francisco International Airport (SFO) air traffic between July 2005 and September 2020. More details about the dataset available in the following post and vignette.

For this analysis, we will use the following packages:

• sfo – the passenger air traffic data
• dplyr – data prep
• plotly – data visualization
• TSstudio – time series analysis and forecasting
library(sfo)
library(dplyr)
library(plotly)
library(TSstudio)

#### Data

As mentioned above, the sfo_passengers dataset provides monthly statistics about the air passenger traffic at SFO airport since 2005. That includes monthly information about the number of passengers by different categories such as operating airline, region, terminal, etc. Let’s load the data:

data("sfo_passengers")

str(sfo_passengers)
## 'data.frame':    22576 obs. of  12 variables:
##  $activity_period : int 202009 202009 202009 202009 202009 202009 202009 202009 202009 202009 ... ##$ operating_airline          : chr  "United Airlines" "United Airlines" "United Airlines" "United Airlines" ...
##  $operating_airline_iata_code: chr "UA" "UA" "UA" "UA" ... ##$ published_airline          : chr  "United Airlines" "United Airlines" "United Airlines" "United Airlines" ...
##  $published_airline_iata_code: chr "UA" "UA" "UA" "UA" ... ##$ geo_summary                : chr  "International" "International" "International" "International" ...
##  $geo_region : chr "Mexico" "Mexico" "Mexico" "Mexico" ... ##$ activity_type_code         : chr  "Enplaned" "Enplaned" "Enplaned" "Deplaned" ...
##  $price_category_code : chr "Other" "Other" "Other" "Other" ... ##$ terminal                   : chr  "Terminal 3" "Terminal 3" "International" "International" ...
##  $boarding_area : chr "F" "E" "G" "G" ... ##$ passenger_count            : int  6712 396 376 6817 3851 3700 71 83 65 45 ...

Before we will convert the data into time series format, we will transform the activity_period into a Date format:

df <- sfo_passengers %>%
mutate(date = as.Date(paste(substr(sfo_passengers$activity_period, 1,4), substr(sfo_passengers$activity_period, 5,6),
"01", sep ="/"))) 

Next, we will transform the dataset into a time series format by grouping the passenger by the date variable:

df <- df %>%
group_by(date) %>%
summarise(y = sum(passenger_count), .groups = "drop")

## # A tibble: 6 x 2
##   date             y
##   <date>       <int>
## 1 2005-07-01 3225769
## 2 2005-08-01 3195866
## 3 2005-09-01 2740553
## 4 2005-10-01 2770715
## 5 2005-11-01 2617333
## 6 2005-12-01 2671797

Now, we have a monthly time series:

plot_ly(data = df,
x = ~ date,
y = ~ y,
type = "scatter",
mode = "line",
name = "Total Passengers") %>%
xend = as.Date("2020-02-01"),
y = min(df$y), yend = max(df$y) * 1.05,
line = list(color = "black", dash = "dash"),
showlegend = FALSE) %>%
x = as.Date("2018-09-01"),
y = max(df$y) * 1.05, showarrow = FALSE) %>% add_annotations(text = "Post-Covid19", x = as.Date("2021-08-01"), y = max(df$y) * 1.05,
showarrow = FALSE) %>%
layout(title = "Total Number of Air Passengers - SFO Airport",
yaxis = list(title = "Number of Passengers"),
xaxis = list(title = "Source: San Francisco Open Data Portal"))

As can be observed in the plot above, the Covid19 effect is well pronounced since March 2020. To quantify the effect of the pandemic on the number of passengers, we will split the series into the following two series:

• Pre-Covid19 series- all observations prior to March 2020
• Post-Covid19 series- all observations post (include) to March 2020

We will use the Pre-Covid19 series to train a time series model. That will enable us to forecast the number of passengers as if there was no pandemic.

pre_covid <- df %>%
dplyr::filter(date < as.Date("2020-03-01")) %>%
dplyr::arrange(date)

post_covid <- df %>%
dplyr::filter(date >= as.Date("2020-03-01")) %>%
dplyr::arrange(date)

We will use the pre_covid series to train a time series model. That will enable us to forecast the number of passengers as there was no pandemic. Once we forecast the corresponding observations of the post_covid series with the pre_covid data, we could quantify the impact of the Covid19 on the total number of passengers.

#### Analyzing the data

Before we forecast the series, let’s run a quick exploratory analysis on the series to identify its main characteristics. We will use the TSstudio package to visualize the pre_covid series. Note that the package does not support, yet, the tsibble object. Therefore, we will convert the series into a ts object first:

ts.obj <- ts(pre_covid\$y, start = c(2005, 7), frequency = 12)

The series before the outbreak of the pandemic:

ts_plot(ts.obj,
title = "Total Number of Air Passengers - SFO Airport",
Ytitle = "Number of Passengers",
slider = TRUE)

Like most series that describes monthly air passenger traffic, the series has a strong monthly seasonal pattern and a positive trend. You can also notice that the seasonal component’s oscillation has become larger since 2017 (compared to previous years). Let’s use the ts_seasonal function to create a seasonal plot of the series:

ts_seasonal(ts.obj = ts.obj, type = "all")

As can see in the seasonal plot, the monthly seasonal effect kept overtime while the series continue to grow from year to year.

Similarly, we can review the series correlation with its past lags using the ACF and PACF functions:

ts_cor(ts.obj = ts.obj, lag.max = 36)

And as expected, we can see strong correlation between the series and the first and seasonal lags. We will leverage this information to select time-series models for seasonal data.

#### Forecast the Pre-Covid19 series

One of my favorite forecasting strategies is a combination of horse racing between different time series models and backtesting as a training approach. Backtesting is the time series equivalent of the machine learning cross-validation training approach. The idea here is simple - test each model with backtesting, and select the one that performed best, on average, on the different testing partition.

The train_model function from the TSstudio package enables us to apply this strategy seamlessly using models from the forecast and stats packages. For simplicity, we will use different flavors of ETS and Holt-Winters models and out-of-the-box auto.arima and tslm models. For the backtesting, we will split the series into 6 testing partitions, each 12 months spaced by 3 months from each other.

The methods argument defines the models to use and the train_method argument defines the setting of the backtesting. Can find more details about the function here.

methods <- list(ets1 = list(method = "ets",
method_arg = list(opt.crit = "lik"),
notes = "ETS model opt.crit=lik"),
ets2 = list(method = "ets",
method_arg = list(opt.crit = "amse"),
notes = "ETS model opt.crit=amse"),
ets3 = list(method = "ets",
method_arg = list(opt.crit = "mse"),
notes = "ETS model opt.crit=mse"),
auto_arima = list(method = "auto.arima",
notes = "Auto ARIMA"),
hw1 = list(method = "HoltWinters",
method_arg = NULL,
notes = "HoltWinters Model"),
hw2 = list(method = "HoltWinters",
method_arg = list(seasonal = "multiplicative"),
notes = "HW with multip. seasonality"),
tslm = list(method = "tslm",
method_arg = list(formula = input ~ trend + season),
notes = "tslm with trend and seasonal"))

train_method = list(partitions = 6,
sample.out = 12,
space = 3)

After we defined the methods and train_method arguments we will use the train_model function to train the models. Note that the forecast horizon is set the the length of the post_covid series. In addition we will set the MAPA as the error metric to evaluate the performance of the different models on the testing partitions:

md <- train_model(input = ts.obj,
methods = methods,
train_method = train_method,
horizon = nrow(post_covid),
error = "MAPE")
## # A tibble: 7 x 7
##   model_id   model       notes                        avg_mape avg_rmse avg_coverage_80% avg_coverage_95%
##   <chr>      <chr>       <chr>                           <dbl>    <dbl>              <dbl>              <dbl>
## 1 hw1        HoltWinters HoltWinters Model              0.0277  149046.              0.792              0.958
## 2 ets2       ets         ETS model opt.crit=amse        0.0284  161754.              0.792              0.972
## 3 hw2        HoltWinters HW with multip. seasonality    0.0300  171702.              0.528              0.847
## 4 ets1       ets         ETS model opt.crit=lik         0.0307  173337.              0.833              0.958
## 5 ets3       ets         ETS model opt.crit=mse         0.0311  174238.              0.861              0.972
## 6 auto_arima auto.arima  Auto ARIMA                     0.0334  184381.              0.597              0.889
## 7 tslm       tslm        tslm with trend and seasonal   0.0370  223194.              0.569              0.75

Based on the leaderboard table from the train_model function, the model that performed best on average on the different testing partitions is the Holt-Winters model (first version - hw1). The model achieved, on average, the lowest MAPE (2.76%) and RMSE (149046) compared to the other models which evaluated. In addition, the model achieved a close to perfect coverage of the model prediction intervals with an average coverage of 79.2% and 95.8% for the 80% and 95% prediction interval, respectively. We can review the error distribution across the different partitions for each model with the plot_error function:

plot_error(md)

The plot_model enables us to animate the forecasted values of each model on the different testing partitions of the backtesting:

plot_model(md)