---
title: "Plotting Seasonality and Correlation"
author: "Matt Dancho"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Plotting_Seasonality_and_Correlation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  fig.width = 8, 
  fig.height = 4.5,
  fig.align = 'center',
  out.width='95%', 
  dpi = 100,
  collapse = TRUE,
  comment = "#>"
)
```

> timetk: A toolkit for time series analysis in R

This tutorial focuses on 3 new functions for visualizing time series diagnostics:

- ACF Diagnostics: `plot_acf_diagnostics()`
- Seasonality Diagnostics: `plot_seasonal_diagnostics()`
- STL Diagnostics: `plot_stl_diagnostics()`

```{r setup}
library(tidyverse)
library(timetk)

# Setup for the plotly charts (# FALSE returns ggplots)
interactive <- FALSE
```

# Correlation Plots

## Grouped ACF Diagnostics

```{r, fig.height=6}
m4_hourly %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        date, value,               # ACF & PACF
        .lags = "7 days",          # 7-Days of hourly lags
        .interactive = interactive
    )
```

## Grouped CCF Plots

```{r, fig.height=8}
walmart_sales_weekly %>%
    select(id, Date, Weekly_Sales, Temperature, Fuel_Price) %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        Date, Weekly_Sales,        # ACF & PACF
        Temperature, Fuel_Price,   # CCFs
        .lags        = "3 months",    # 3 months of weekly lags
        .interactive = interactive
    )
```

# Seasonality

## Seasonal Visualizations

```{r, fig.height=8}
taylor_30_min %>%
    plot_seasonal_diagnostics(date, value, .interactive = interactive)
```

## Grouped Seasonal Visualizations

```{r, fig.height=8}
m4_hourly %>%
    group_by(id) %>%
    plot_seasonal_diagnostics(date, value, .interactive = interactive)
```


# STL Diagnostics

```{r, fig.height=8}
m4_hourly %>%
    group_by(id) %>%
    plot_stl_diagnostics(
        date, value,
        .frequency = "auto", .trend = "auto",
        .feature_set = c("observed", "season", "trend", "remainder"),
        .interactive = interactive)
```

# Learning More

If you are interested in learning from my advanced __Time Series Analysis & Forecasting Course__, then [join my waitlist](https://mailchi.mp/business-science/time-series-forecasting-course-coming-soon). The course is coming soon. 

```{r, echo=FALSE}
knitr::include_graphics("time_series_course.jpg")
```

You will learn:

- Time Series Preprocessing, Noise Reduction, & Anomaly Detection
- Feature engineering using lagged variables & external regressors
- Hyperparameter Tuning
- Time series cross-validation
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- NEW - Deep Learning with RNNs (Competition Winner)
- and more.

<p class="text-center" style="font-size:30px;">
<a href="https://mailchi.mp/business-science/time-series-forecasting-course-coming-soon">Signup for the Time Series Course waitlist</a>
</p>
