# Time Series in 5-Minutes, Part 1: Data Wrangling and Rolling Calculations

*Written by Matt Dancho on August 19, 2020*

**Have 5-minutes? Then let’s learn time series.** In this short articles series, I highlight how you can get up to speed quickly on important aspects of time series analysis. Today we are focusing preparing data for timeseries analysis rolling calculations.

### Updates

This article has been updated. View the updated Time Series in 5-Minutes article at Business Science.

## Time Series in 5-Mintues

Articles in this Series

I just released `timetk`

2.0.0 (read the release announcement). A ton of new functionality has been added. We’ll discuss some of the key pieces in this article series:

- Part 1, Data Wrangling and Rolling Calculations
- Part 2, The Time Plot
- Part 3, Autocorrelation
- Part 4, Seasonality
- Part 5, Anomalies and Anomaly Detection
- Part 6, Modeling Time Series Data

👉 **Register for our blog to get new articles as we release them.**

# Have 5-Minutes?

Then let’s learn Rolling Calculations

A collection of tools for working with time series in R Time series data wrangling is an essential skill for any forecaster.

`timetk`

includes the essential data wrangling tools. In this tutorial:

**Summarise by Time**- For time-based aggregations**Filter by Time**- For complex time-based filtering**Pad by Time**- For filling in gaps and going from*low to high frequency***Slidify**- For turning any function into a sliding (rolling) function

Additional concepts covered:

**Imputation**- Needed for Padding (See Low to High Frequency)**Advanced Filtering**- Using the new add time`%+time`

infix operation (See*Padding Data: Low to High Frequency*)**Visualization**-`plot_time_series()`

for all visualizations

# Let’s Get Started

# Data

This tutorial will use the `FANG`

dataset:

- Daily
- Irregular (missing business holidays and weekends)
- 4 groups (FB, AMZN, NFLX, and GOOG).

The adjusted column contains the adjusted closing prices for each day.

The volume column contains the trade volume (number of times the stock was transacted) for the day.

# Summarize by Time

`summarise_by_time()`

aggregates by a period. It’s great for:

- Period Aggregation -
`SUM()`

- Period Smoothing -
`AVERAGE()`

,`FIRST()`

,`LAST()`

## Period Summarization

Objective: Get the total trade volume by quarter

- Use
`SUM()`

- Aggregate using
`.by = "quarter"`

## Period Smoothing

Objective: Get the first value in each month

- We can use
`FIRST()`

to get the first value, which has the effect of reducing the data (i.e. smoothing). We could use`AVERAGE()`

or`MEDIAN()`

. - Use the summarization by time:
`.by = "month"`

to aggregate by month.

# Filter By Time

Used to quickly filter a continuous time range.

## Time Range Filtering

Objective: Get the adjusted stock prices in the 3rd quarter of 2013.

`.start_date = "2013-09"`

: Converts to “2013-09-01`.end_date = "2013"`

: Converts to “2013-12-31- A more advanced example of filtering using
`%+time`

and`%-time`

is shown in*“Padding Data: Low to High Frequency”*.

# Padding Data

Used to fill in (pad) gaps and to go from from low frequency to high frequency. This function uses the awesome `padr`

library for filling and expanding timestamps.

## Fill in Gaps

Objective: Make an irregular series regular.

- We will leave padded values as
`NA`

. - We can add a value using
`.pad_value`

or we can impute using a function like`ts_impute_vec()`

(shown next).

## Low to High Frequency

Objective: Go from Daily to Hourly timestamp intervals for 1 month from the start date. Impute the missing values.

`.by = "hour"`

pads from daily to hourly- Imputation of hourly data is accomplished with
`ts_impute_vec()`

, which performs linear interpolation when`period = 1`

. - Filtering is accomplished using:
- “start”: A special keyword that signals the start of a series
`FIRST(date) %+time% "1 month"`

: Selecting the first date in the sequence then using a special infix operation,`%+time%`

, called “add time”. In this case I add “1 month”.

# Sliding (Rolling) Calculations

We have a new function, `slidify()`

that turns any function into a sliding (rolling) window function. It takes concepts from `tibbletime::rollify()`

and it improves them with the R package `slider`

.

## Rolling Mean

Objective: Calculate a “centered” simple rolling average with partial window rolling and the start and end windows.

`slidify()`

turns the`AVERAGE()`

function into a rolling average.

For simple rolling calculations (rolling average), we can accomplish this operation faster with `slidify_vec()`

- A vectorized rolling function for simple summary rolls (e.g. `mean()`

, `sd()`

, `sum()`

, etc)

## Rolling Regression

Objective: Calculate a rolling regression.

- This is a complex sliding (rolling) calculation that requires multiple columns to be involved.
`slidify()`

is built for this.- Use the multi-variable
`purrr`

`..1`

,`..2`

,`..3`

, etc notation to setup a function

# Have questions on using Timetk for time series?

Make a comment in the chat below. 👇

And, if you plan on using `timetk`

for your business, it’s a no-brainer - Join the Time Series Course.