Demo Week: Time Series Machine Learning with timetk
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We’re into the second day of Business Science Demo Week. What’s demo week? Every day this week we are demoing an R package: tidyquant
(Monday), timetk
(Tuesday), sweep
(Wednesday), tibbletime
(Thursday) and h2o
(Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Second up is timetk
, your toolkit for time series in R. Here we go!
Previous Demo Week Demos
timetk: What’s It Used For?
There are three main uses:

Time series machine learning: Using regression algorithms to forecast

Making future time series indicies: Extract, explore, and extend a time series index using patterns in the timebase

Coercing (converting) between time classes (e.g. between
tbl
,xts
,zoo
,ts
): Consistent coercion makes working in the various time classes much easier!
We’ll go over time series ML and coercion today. The second (extracting and making future time series) will be touched on in time series ML as this is very critical to prediction accuracy.
Load Libraries
We’ll need two libraries today:
tidyquant
: For getting data and loading the tidyverse behind the scenestimetk
: Toolkit for working with time series in R
If you haven’t done so already, install the packages:
Load the libraries.
Data
We’ll get data using the tq_get()
function from tidyquant
. The data comes from FRED: Beer, Wine, and Distilled Alcoholic Beverages Sales.
It’s a good idea to visualize the data so we know what we’re working with. Visualization is particularly important for time series analysis and forecasting (as we see during time series machine learning). We’ll use tidyquant
charting tools: mainly geom_ma(ma_fun = SMA, n = 12)
to add a 12period simple moving average to get an idea of the trend. We can also see there appears to be both trend (moving average is increasing in a relatively linear pattern) and some seasonality (peaks and troughs tend to occur at specific months).
Now that you have a feel for the time series we’ll be working with today, let’s move onto the demo!
DEMO: timetk
We’ve split this demo into two parts. First, we’ll follow a workflow for time series machine learning. Second, we’ll check out coercion tools.
Part 1: Time Series Machine Learning
Time series machine learning is a great way to forecast time series data, but before we get started here are a couple pointers for this demo:

Key Insight: The time series signature ~ timestamp information expanded columnwise into a feature set ~ is used to perform machine learning.

Objective: We’ll predict the next 12 months of data for the time series using the time series signature.
We’ll go through a workflow that can be used to perform time series machine learning. You’ll see how several timetk
functions can help with this process. We’ll do machine learning with a simple lm()
linear regression, and you will see how powerful and accurate this can be when a time series signature is used. Further, you should think about what other more powerful machine learning algorithms can be used such as xgboost
, glmnet
(LASSO), and others.
Step 0: Review data
Just to show our starting point, let’s print out our beer_sales_tbl
.
We can quickly get a feel for the time series using tk_index()
to extract the index and tk_get_timeseries_summary()
to retrieve summary information of the index. We use glimpse()
to output in a nice format for review.
We can see important features like start, end, units, etc. We also have the quantiles of the timediffs (difference in seconds between observations), which is useful for assessing the degree of regularity. Because the scale is monthly, the number of seconds between each month follows an irregular distribution.
Step 1: Augment Time Series Signature
The tk_augment_timeseries_signature()
function expands out the timestamp information columnwise into a machine learning feature set, adding columns of time series information to the original data frame.
Step 2: Model
Apply any regression model to the data. We’ll use lm()
. Note that we drop the date and diff columns. Most algorithms do not work with dates, and the diff column is not useful for machine learning (it’s more useful for finding time gaps in the data).
Step 3: Build Future (New) Data
Use tk_index()
to extract the index.
Make a future index from the existing index with tk_make_future_timeseries
. The function internally checks the periodicity and returns the correct sequence. Note that we have a whole vignette on how to make future time series, which is helpful due to the complexity of the topic.
From the future index, use tk_get_timeseries_signature()
to turn index into time signature data frame.
Step 4: Predict the New Data
Use the predict()
function for your regression model. Note that we drop the index and diff columns, the same as before when using the lm()
function.
Step 5: Compare Actual vs Predictions
We can use tq_get()
to retrieve the actual data. Note that we don’t have all of the data for comparison, but we can at least compare the first several months of actual values.
Visualize our forecast.
We can investigate the error on our test set (actuals vs predictions).
And we can calculate a few residuals metrics. The MAPE error is approximately 4.5% from the actual value, which is pretty good for a simple multivariate linear regression. A more complex algorithm could produce more accurate results.
Time series machine learning can produce exceptional forecasts. For those interested in learning more, we have a whole vignette dedicated to time series forecasting using timetk.
Part 2: Coercion

Problem: Switching between various time classes in R is painful and inconsistent.

Solution:
tk_tbl
,tk_xts
,tk_zoo
,tk_ts
tk_xts
We are starting with a tbl
object. A disadvantage is that sometimes we would like to convert to an xts object to use xtsbased functions from the numerous packages that deal with xts objects (xts
, zoo
, quantmod
, etc).
We can easily convert to an xts
object using tk_xts()
. Notice that tk_xts()
autodetects the timebased column and uses its values as the index for the xts object.
We can also go from xts
back to tbl
. We tack on rename_index = "date"
to have the index name match what we started with. This used to be very difficult. Notice that
tk_ts
A number of packages use a different time class called ts
. Probably the most popular is the forecast
package. The advantage of using the tk_ts()
function is twofold:
 It’s consistent with the other
tk_
coercion functions so coercing back and forth is straightforward and easy.  IMPORTANT: When
tk_ts()
is used, the tsobject carries the original irregular time index (usually dates) as an index attribute. This makes keeping date and datetime information possible.
Here’s an example. We can use tk_ts()
to convert to a ts
object. Because the tsbased system only works with regular time series, we need to add the arguments start = 2010
and freq = 12
.
There are two ways we can go back to tbl
:
 Just coerce back using
tk_tbl()
and we get the “regular” index as YEARMON data type fromzoo
.  If the object was created with
tk_ts()
and has atimetk_index
, we can coerce back usingtk_tbl(timetk_index = TRUE)
and we get the original “irregular” index as Date data type.
Method 1: We go back to tbl
. Note that the date column is YEARMON class.
Method 2: We go back to tbl
but specify timetk_idx = TRUE
to return original DATE or DATETIME information.
First, you can check to see if the tsobject has a timetk index with has_timetk_idx()
.
If TRUE
, then specify timetk_idx = TRUE
during the tk_tbl()
coercion. See that we now have “date” data type. This was previously very difficult to do.
Next Steps
We’ve only scratched the surface of timetk
. There’s more to learn including working with time series indices and making future indices. Here are a few resources to help you along the way:
 Business Science Software Website
 timetk documentation  The vignettes are must reads for time series machine learning, working with the index and creating future index that matched the desired time series pattern
 timetk GitHub Page
 Business Science Insights Blog
Announcements
We have a busy couple of weeks. In addition to Demo Week, we have:
DataTalk
On Thursday, October 26 at 7PM EST, Matt will be giving a FREE LIVE #DataTalk on Machine Learning for Recruitment and Reducing Employee Attrition. You can sign up for a reminder at the Experian Data Lab website.
#MachineLearning for Reducing Employee Attrition @BizScienchttps://t.co/vlxmjWzKCL#ML #AI #HR #IoTT #IoT #DL #BigData #Tech #Cloud #Jobs pic.twitter.com/dF5Znf10Sk
— Experian DataLab (@ExperianDataLab) October 18, 2017
EARL
On Friday, November 3rd, Matt will be presenting at the EARL Conference on HR Analytics: Using Machine Learning to Predict Employee Turnover.
?Hey #rstats. I'll be presenting @earlconf on #MachineLearning applications in #HumanResources. Get 15% off tickets: https://t.co/b6JUQ6BSTl
— Matt Dancho (@mdancho84) October 11, 2017
Courses
Based on recent demand, we are considering offering applicationspecific machine learning courses for Data Scientists. The content will be business problems similar to our popular articles:

HR Analytics: Using Machine Learning to Predict Employee Turnover

Sales Analytics: How To Use Machine Learning to Predict and Optimize Product Backorders
The student will learn from Business Science how to implement cutting edge data science to solve business problems. Please let us know if you are interested. You can leave comments as to what you would like to see at the bottom of the post in Disqus.
About Business Science
Business Science specializes in “ROIdriven data science”. Our focus is machine learning and data science in business applications. We help businesses that seek to add this competitive advantage but may not have the resources currently to implement predictive analytics. Business Science works with clients primarily in small to medium size businesses, guiding these organizations in expanding predictive analytics while executing on ROI generating projects. Visit the Business Science website or contact us to learn more!
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