---
title: "Introduction to sweep"
author: "Matt Dancho"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Introduction to sweep}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(
    # message = FALSE,
    # warning = FALSE,
    fig.width = 8, 
    fig.height = 4.5,
    fig.align = 'center',
    out.width='95%', 
    dpi = 200
)

# devtools::load_all() # Travis CI fails on load_all()
```

> Extending `broom` to time series forecasting

The `sweep` package extends the `broom` tools (tidy, glance, and augment) for performing forecasts and time series analysis in the "tidyverse". The package is geared towards the workflow required to perform forecasts using Rob Hyndman's `forecast` package, and contains the following elements:

1. __model tidiers__: `sw_tidy`, `sw_glance`, `sw_augment`, `sw_tidy_decomp` functions extend `tidy`, `glance`, and `augment` from the `broom` package specifically for models (`ets()`, `Arima()`, `bats()`, etc) used for forecasting. 

2. __forecast tidier__: `sw_sweep` converts a `forecast` object to a tibble that can be easily manipulated in the "tidyverse".

To illustrate, let's take a basic forecasting workflow starting from data collected in a tibble format and then performing a forecast to achieve the end result in tibble format.

# Prerequisites

Before we get started, load the following packages.

```{r, message = F}
library(ggplot2)
library(tidyquant)
library(timetk)
library(sweep)
library(forecast)
```

# Forecasting Monthly Bike Sales Revenue

We'll use the `bike_sales` data set that ships with `sweep` and aggregate it to monthly revenue. This gives us a local, reproducible series with both seasonality and trend.

```{r}
monthly_sales_tbl <- bike_sales %>%
    dplyr::mutate(date = lubridate::floor_date(order.date, unit = "month")) %>%
    dplyr::group_by(date) %>%
    dplyr::summarise(price = sum(price.ext), .groups = "drop")
monthly_sales_tbl
```

We can quickly visualize using the `ggplot2` package. We can see that there appears to be some seasonality and an upward trend.

```{r}
monthly_sales_tbl %>%
    ggplot(aes(x = date, y = price)) +
    geom_line(linewidth = 1, color = palette_light()[[1]]) +
    geom_smooth(method = "loess") +
    labs(title = "Bike Sales Revenue: Monthly", x = "", y = "Revenue") +
    scale_y_continuous(labels = scales::label_dollar(scale = 1 / 1000000, suffix = "M")) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
    theme_tq()
```


# Forecasting Workflow

The forecasting workflow involves a few basic steps:

1. Step 1: Coerce to a `ts` object class.
2. Step 2: Apply a model (or set of models)
3. Step 3: Forecast the models (similar to predict)
4. Step 4: Use `sw_sweep()` to tidy the forecast.

_Note that we purposely omit other steps such as testing the series for stationarity (`Box.test(type = "Ljung")`) and analysis of autocorrelations (`Acf`, `Pacf`) for brevity purposes. We recommend the analyst to follow the forecasting workflow in ["Forecasting: principles and practice"](https://otexts.com/fpp2/)_

## Step 1: Coerce to a `ts` object class

The `forecast` package uses the `ts` data structure, which is quite a bit different than tibbles that we are currently using. Fortunately, it's easy to get to the correct structure with `tk_ts()` from the `timetk` package. The `start` and `freq` variables are required for the regularized time series (`ts`) class, and these specify how to treat the time series. For monthly, the frequency should be specified as 12. This results in a nice calendar view. The `silent = TRUE` tells the `tk_ts()` function to skip the warning notifying us that the "date" column is being dropped. Non-numeric columns must be dropped for `ts` class, which is matrix based and a homogeneous data class.


```{r}
monthly_sales_ts <- tk_ts(monthly_sales_tbl, start = 2011, freq = 12, silent = TRUE)
monthly_sales_ts
```

A significant benefit is that the resulting `ts` object maintains a "timetk index", which will help with forecasting dates later. We can verify this using `has_timetk_idx()` from the `timetk` package.

```{r}
has_timetk_idx(monthly_sales_ts)
```


Now that a time series has been coerced, let's proceed with modeling.

## Step 2: Modeling a time series

The modeling workflow takes a time series object and applies a model. Nothing new here: we'll simply use the `ets()` function from the `forecast` package to get an Exponential Smoothing ETS (Error, Trend, Seasonal) model.

```{r}
fit_ets <- monthly_sales_ts %>%
    ets()
```

Where `sweep` can help is in the evaluation of a model. Expanding on the `broom` package there are four functions:

* `sw_tidy()`: Returns a tibble of model parameters
* `sw_glance()`: Returns the model accuracy measurements
* `sw_augment()`: Returns the fitted and residuals of the model
* `sw_tidy_decomp()`: Returns a tidy decomposition from a model

The guide below shows which model object compatibility with `sweep` tidier functions.

```{r, echo = F}
tibble::tribble(
    ~Object,       ~`sw_tidy()`, ~`sw_glance()`, ~`sw_augment()`, ~`sw_tidy_decomp()`, ~`sw_sweep()`,
    "ar",          "",  "",  "", "",   "",
    "arima",       "X", "X", "X", "",  "",
    "Arima",       "X", "X", "X", "",  "",
    "ets",         "X", "X", "X", "X", "",
    "baggedETS",   "",  "",  "",  "",  "",
    "bats",        "X", "X", "X", "X", "",
    "tbats",       "X", "X", "X", "X", "",
    "nnetar",      "X", "X", "X", "",  "",
    "stl",         "",  "",  "",  "X", "",
    "HoltWinters", "X", "X", "X", "X", "",
    "StructTS",      "X", "X", "X", "X", "",
    "tslm",        "X", "X", "X", "",  "",
    "decompose",   "",  "",  "",  "X", "",
    "adf.test",    "X", "X", "",  "",  "",
    "Box.test",    "X", "X", "",  "",  "",
    "kpss.test",   "X", "X", "",  "",  "",
    "forecast",    "",  "",  "",  "",  "X"
) %>%
    knitr::kable(caption = "Function Compatibility",
                 align = c("l", "c", "c", "c", "c", "c"))
```


Going through the tidiers, we can get useful `model` information. 

### sw_tidy

`sw_tidy()` returns the model parameters.

```{r}
sw_tidy(fit_ets)
```

### sw_glance

`sw_glance()` returns the model quality parameters.

```{r}
sw_glance(fit_ets)
```

### sw_augment

`sw_augment()` returns the actual, fitted and residual values.

```{r}
augment_fit_ets <- sw_augment(fit_ets)
augment_fit_ets
```

We can review the residuals to determine if their are any underlying patterns left. Note that the index is class `yearmon`, which is a regularized date format.  

```{r}
augment_fit_ets %>%
    ggplot(aes(x = index, y = .resid)) +
    geom_hline(yintercept = 0, color = "grey40") +
    geom_point(color = palette_light()[[1]], alpha = 0.5) +
    geom_smooth(method = "loess") +
    scale_x_yearmon(n = 10) +
    labs(title = "Bike Sales Revenue: ETS Residuals", x = "") + 
    theme_tq()
```


### sw_tidy_decomp

`sw_tidy_decomp()` returns the decomposition of the ETS model.

```{r}
decomp_fit_ets <- sw_tidy_decomp(fit_ets)
decomp_fit_ets 
```

We can review the decomposition using `ggplot2` as well.  The data will need to be manipulated slightly for the facet visualization. The `gather()` function from the `tidyr` package is used to reshape the data into a long format data frame with column names "key" and "value" indicating all columns except for index are to be reshaped. The "key" column is then mutated using `mutate()` to a factor which preserves the order of the keys so "observed" comes first when plotting. 

```{r}
decomp_fit_ets %>%
    tidyr::gather(key = key, value = value, -index) %>%
    dplyr::mutate(key = as.factor(key)) %>%
    ggplot(aes(x = index, y = value, group = key)) +
    geom_line(color = palette_light()[[2]]) +
    geom_ma(ma_fun = SMA, n = 12, size = 1) +
    facet_wrap(~ key, scales = "free_y") +
    scale_x_yearmon(n = 10) +
    labs(title = "Bike Sales Revenue: ETS Decomposition", x = "") + 
    theme_tq() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

Under normal circumstances it would make sense to refine the model at this point. However, in the interest of showing capabilities (rather than how to forecast) we move onto forecasting the model. For more information on how to forecast, please refer to the online book [_"Forecasting: principles and practices"_](https://otexts.com/fpp2/). 

## Step 3: Forecasting the model

Next we forecast the ETS model using the `forecast()` function. The returned `forecast` object isn't in a "tidy" format (i.e. data frame). This is where the `sw_sweep()` function helps.

```{r}
fcast_ets <- fit_ets %>%
    forecast(h = 12) 
```

## Step 4: Tidy the forecast object

We'll use the `sw_sweep()` function to coerce a `forecast` into a "tidy" data frame. The `sw_sweep()` function then coerces the `forecast` object into a tibble that can be sent to `ggplot` for visualization. Let's inspect the result.

```{r}
sw_sweep(fcast_ets, fitted = TRUE)
```

The tibble returned contains "index", "key" and "value" (or in this case "price") columns in a long or "tidy" format that is ideal for visualization with `ggplot2`. The "index" is in a regularized format (in this case `yearmon`) because the `forecast` package uses `ts` objects. We'll see how we can get back to the original irregularized format (in this case `date`) later. The "key" and "price" columns contains three groups of key-value pairs: 

1. __actual__: the actual values from the original data
2. __fitted__: the model values returned from the `ets()` function (excluded by default) 
3. __forecast__: the predicted values from the `forecast()` function

The `sw_sweep()` function contains an argument `fitted = FALSE` by default meaning that the model "fitted" values are not returned. We can toggle this on if desired. The remaining columns are the forecast confidence intervals (typically 80 and 95, but this can be changed with `forecast(level = c(80, 95))`). These columns are setup in a wide format to enable using the `geom_ribbon()`. 


Let's visualize the forecast with `ggplot2`. We'll use a combination of `geom_line()` and `geom_ribbon()`. The fitted values are toggled off by default to reduce the complexity of the plot, but these can be added if desired. Note that because we are using a regular time index of the `yearmon` class, we need to add `scale_x_yearmon()`.

```{r}
sw_sweep(fcast_ets) %>%
    ggplot(aes(x = index, y = price, color = key)) +
    geom_ribbon(aes(ymin = lo.95, ymax = hi.95), 
                fill = "#D5DBFF", color = NA, linewidth = 0) +
    geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), 
                fill = "#596DD5", color = NA, linewidth = 0, alpha = 0.8) +
    geom_line(linewidth = 1) +
    labs(title = "Bike Sales Revenue, ETS Model Forecast", x = "", y = "Revenue",
         subtitle = "Regular Time Index") +
    scale_y_continuous(labels = scales::label_dollar(scale = 1 / 1000000, suffix = "M")) +
    scale_x_yearmon(n = 12, format = "%Y") +
    scale_color_tq() +
    scale_fill_tq() +
    theme_tq() 
```

Because the `ts` object was created with the `tk_ts()` function, it contained a timetk index that was carried with it throughout the forecasting workflow. As a result, we can use the `timetk_idx` argument, which maps the original irregular index (dates) and a generated future index to the regularized time series (yearmon). This results in the ability to return an index of date and datetime, which is not currently possible with the `forecast` objects. Notice that the index is returned as `date` class.

```{r}
sw_sweep(fcast_ets, timetk_idx = TRUE) %>%
    head()
```

```{r}
sw_sweep(fcast_ets, timetk_idx = TRUE) %>%
    tail()
```

We can build the same plot with dates in the x-axis now. 

```{r}
sw_sweep(fcast_ets, timetk_idx = TRUE) %>%
    ggplot(aes(x = index, y = price, color = key)) +
    geom_ribbon(aes(ymin = lo.95, ymax = hi.95), 
                fill = "#D5DBFF", color = NA, linewidth = 0) +
    geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), 
                fill = "#596DD5", color = NA, linewidth = 0, alpha = 0.8) +
    geom_line(linewidth = 1) +
    labs(title = "Bike Sales Revenue, ETS Model Forecast", x = "", y = "Revenue", 
         subtitle = "Irregular Time Index") +
    scale_y_continuous(labels = scales::label_dollar(scale = 1 / 1000000, suffix = "M")) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
    scale_color_tq() +
    scale_fill_tq() +
    theme_tq() 
```

In this example, there is not much benefit to returning an irregular time series. However, when working with frequencies below monthly, the ability to return irregular index values becomes more apparent. 

# Recap

This was an overview of how various functions within the `sweep` package can be used to assist in forecast analysis. In the next vignette, we discuss some more powerful concepts including forecasting at scale with grouped time series analysis. 
