README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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# tscv

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The package `tscv` provides a collection of functions and tools for time series analysis and forecasting as well as time series cross-validation. This is mainly a set of wrapper and helper functions as well as some extensions for the packages `tsibble`, `fable` and `fabletools` that I find useful for research in the area of time series forecasting.

***Disclaimer:*** The `tscv` package is highly experimental and it is very likely that there will be (substantial) changes in the near future.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("ahaeusser/tscv")
```
## Example

```{r packages, message = FALSE, warning = FALSE}
# Load relevant packages
library(tscv)
library(tidyverse)
library(tsibble)
library(fable)
library(feasts)
```

```{r abbreviations, echo=FALSE, warning=FALSE, message=FALSE, results='hide'}
Sys.setlocale("LC_TIME", "C")
```


## Data preparation

The data set `elec_price` is a `tibble` with day-ahead electricity spot prices in [EUR/MWh] from the ENTSO-E Transparency Platform. The data set contains hourly time series data from 2019-01-01 to 2020-12-31 for 8 European bidding zones (BZN): 

* DE: Germany (including Luxembourg)
* DK: Denmark
* ES: Spain
* FI: Finland
* FR: France
* NL: Netherlands
* NO1: Norway 1 (Oslo)
* SE1: Sweden 1 (Lulea)

In this vignette, we will use only four time series to demonstrate the functionality of the package (the data set is filtered to the bidding zones Germany, France, Norway and Sweden). You can use the function `plot_line()` to visualize the four time series. The function `summarise_data()` is used to explore the structure (start date, end date, number of observations and the number missing and zero values). The function `summarise_stats()` calculates descriptive statistics for each time series.

```{r clean_data}

series_id = "bidding_zone"
value_id = "value"
index_id = "time"

context <- list(
  series_id = series_id,
  value_id = value_id,
  index_id = index_id
)

# Prepare data set
main_frame <- elec_price %>%
  filter(bidding_zone %in% c("DE", "FR", "NO1", "SE1"))

main_frame

main_frame %>%
  plot_line(
    x = time,
    y = value,
    color = bidding_zone,
    facet_var = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    subtitle = "2019-01-01 to 2020-12-31",
    xlab = "Time",
    ylab = "[EUR/MWh]",
    caption = "Data: ENTSO-E Transparency"
    )

summarise_data(
  .data = main_frame,
  context = context
)

summarise_stats(
  .data = main_frame,
  context = context
)

```


## Split data into training and testing

To prepare the data set for time series cross-validation (TSCV), you can use the function `make_split()`. This function splits the data into several slices for training and testing (i.e. partitioning into time slices) for time series cross-validation. You can choose between `stretch` and `slide`. The first is an expanding window approach, while the latter is a fixed window approach. Furthermore, we define the (initial) window size for training and testing via `n_init` and `n_ahead`, as well as the step size for increments via `n_skip`. Further options for splitting the data are available via `type` (see function reference for more details). 

```{r split_data}

# Setup for time series cross validation
type = "first"
value = 2400      # size for training window
n_ahead = 24      # size for testing window (= forecast horizon)
n_skip = 23       # skip 23 observations
n_lag = 0         # no lag
mode = "slide"    # fixed window approach
exceed = FALSE    # only pseudo out-of-sample forecast

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = type,
  value = value,
  n_ahead = n_ahead,
  n_skip = n_skip,
  n_lag = n_lag,
  mode = mode,
  exceed = exceed
)

# For illustration, only the first 50 splits are used
split_frame <- split_frame %>%
  filter(split %in% c(1:50))

split_frame

```



## Training and forecasting

The training and test splits are prepared within `split_frame` and we are ready for forecasting. The function `slice_train()` slices the data `main_frame` according to the splits within `split_frame`. As we are using forecasting methods from the packages `fable` and `fabletools`, we have to convert the data set `main_frame` from a `tibble` to a `tsibble`. Due to the sample size and computation time, only very simple benchmark methods are used: 

* `SNAIVE`: Seasonal naive model with weekly seasonality (from package `fable`)
* `STL-NAIVE`: STL-decomposition model and naive forecast. The series is decomposed via STL and the seasonal adjusted series is predicted via the naive approach. Afterwards, seasonal component is added to the forecasts (from packages `fable` and `feasts`)
* `SNAIVE2`: Variation of the seasonal naive approach. Mondays, Saturdays and Sundays are treated with a weekly lag. Tuesdays, Wednesdays, Thursdays and Fridays are treated with a daily lag.
* `SMEDIAN`: Seasonal median model

The functions `SMEDIAN()` and `SNAIVE2()` are extensions to the `fable` package

```{r train_models}

# Slice training data from main_frame according to split_frame
train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
  )

train_frame

# Convert tibble to tsibble
train_frame <- train_frame %>%
  as_tsibble(
    index = time,
    key = c(bidding_zone, split)
    )

train_frame

# Model training via fabletools::model()
model_frame <- train_frame %>%
  model(
    "SNAIVE" = SNAIVE(value ~ lag("week")),
    "STL-NAIVE" = decomposition_model(STL(value), NAIVE(season_adjust)),
    "SNAIVE2" = SNAIVE2(value),
    "SMEDIAN" = SMEDIAN(value ~ lag("week"))
    )

model_frame

# Forecasting via fabletools::forecast()
fable_frame <- model_frame %>%
  forecast(h = n_ahead)

fable_frame

# Convert fable_frame (fable) to future_frame (tibble)
future_frame <- make_future(
  fable = fable_frame,
  context = context
  )

future_frame


```


## Evaluation of forecast accuracy

To evaluate the forecast accuracy, the function `make_accuracy()` is used. You can define whether to evaluate the accuracy by `horizon` or by `split`. Several accuracy metrics are available:

* `ME`: mean error
* `MAE`: mean absolute error
* `MSE`: mean squared error
* `RMSE`: root mean squared error
* `MAPE`: mean absolute percentage error
* `sMAPE`: scaled mean absolute percentage error
* `MPE`: mean percentage error
* `rMAE`: relative mean absolute error (relative to some user-defined benchmark method)


### Forecast accuracy by forecast horizon

```{r accuracy_horizon}

# Estimate accuracy metrics by forecast horizon
accuracy_horizon <- make_accuracy(
  future_frame = future_frame,
  main_frame = main_frame,
  context = context,
  dimension = "horizon"
)

accuracy_horizon

# Visualize results
accuracy_horizon %>%
  filter(metric == "MAE") %>%
  plot_line(
    x = n,
    y = value,
    facet_var = bidding_zone,
    facet_nrow = 1,
    color = model,
    title = "Evaluation of forecast accuracy by forecast horizon",
    subtitle = "Mean absolute error (MAE)",
    xlab = "Forecast horizon (n-step-ahead)",
    caption = "Data: ENTSO-E Transparency, own calculation"
    )

```


### Forecast accuracy by split

```{r accuracy_split}

# Estimate accuracy metrics by forecast horizon
accuracy_split <- make_accuracy(
  future_frame = future_frame,
  main_frame = main_frame,
  context = context,
  dimension = "split"
)

accuracy_split

# Visualize results
accuracy_split %>%
  filter(metric == "MAE") %>%
  plot_line(
    x = n,
    y = value,
    facet_var = bidding_zone,
    facet_nrow = 1,
    color = model,
    title = "Evaluation of forecast accuracy by split",
    subtitle = "Mean absolute error (MAE)",
    xlab = "Split",
    caption = "Data: ENTSO-E Transparency, own calculation"
    )

```