Forecasting Time Series With R - Dataiku

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HOWTO

Forecasting time series with R

July 22, 2015

Forecasting Time Series Data With R And

Data Science StudioDo you day-trade stocks? Monitor humidity in the Amazon rainforest? Predict

weekly orange production in the Florida keys? If so, you're using time series!

A time series is when you measure the same variable at regular intervals. They

occur everywhere in data science. R has several great packages that are built

specifically to handle time series data.

This tutorial walks through a time series analysis in R using Data Science Studio.

I'm going to show you how to explore time series data, choose an appropriate

modeling method and deploy the model in DSS. Let's get started!

Preparing The Data

I'm using a dataset with the monthly totals for international airline passengers

(https://datamarket.com/data/set/22u3/international-airline-passengers-

monthly-totals-in-thousands-jan-49-dec-60) provided by datamarket

(https://datamarket.com/). When I upload the data into DSS, it automatically

recognizes the Month column as a date that needs parsing. Pretty cool.

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A simple preparation step can convert this date to the standard format. See our

documentation on dates (https://doc.dataiku.com/dss/latest/preparation/dates.html) for more information on this step.

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Great! Now our data is cleaned and ready for analysis.

Plotting

First, I'm going to create a chart to get a feel for the data. To do this, I click on

!"#$%"!'"(&!%)!"$(*&++$",$%+(-)$&"$. and then the Analyse icon.

Then I'm going to click on charts at the top, and drag /'"#0(*&%+$. into the field

for the x-axis and 1"#$%"!'" &!%)!"$ *&++$,$%+ into the y-axis. Afer a bit of

tweaking, we have the line chart shown below.

We see two really interesting patterns. First, there's a general upward trend in

the number of passengers. Second, there is a yearly cycle with the lowest

number of passengers occuring around the new year and the highest number of

passengers during the late-summer. Let's see if we can use these trends to

forecast the number of passengers afer 1960.

Interactive Analysis With R

To start a notebook, I go back to the flow, click on the

!"#$%"!'"(&!%)!"$(*&++$",$%+(-)$&"$. data set, click on the R icon and then

click "Notebook Interactive visualisation and analysis of your data".

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DSS will then open an R notebook with some basic starter coded already filled

in.

Sweet. Now that we have an R notebook, I'm going to stop those screen shots

and just show the code. You can type the following code into the iPython

notebook for interactive analysis.

First, I'm going to load the R libraries that we need for this analysis. The .&!34

library lets us read and write datasets to DSS. The 5'%$-&+# library has thefunctions we need for training models to predict time series. The .*)6% package

has functions for manipulating data.frames.

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!"#$%$&7.&!348

!"#$%$&75'%$-&+#8

!"#$%$&7.*)6%8

Then, I'm going to load the data into R from DSS

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Excellent. We have our time series. It's time to start modeling!

Choosing a forecasting model

I'm going to try three diff erent forecasting methods and deploy the best to DSS.

In general, it's good practice to try several diff erent modeling methods and go

with whichever provides the best performance.

Model 1: Exponential State Smoothing

The $#+78 function in the 5'%$-&+# package fits exponential state smoothing

(ETS) models. This function automically optimizes the choice of model and

necessary parameters. All you have to do is providing it with a time series.

Let's use it and then make a forecast for the next 24 months.

M($#+ E $#+7#+(*&++$",$%+8

5($#+ E 5'%$-&+#7M($#+G 0ELJ8 ! #$%&'()* +, -$.*/) 0.*$ */& #1*1%&

*)'#75($#+8

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Looking good! The forecast is shown in blue with the grey area representing a95% confidence interval. Just by looking, we see that the forecast roughly

matches the historical pattern of the data.

Model 2: ARIMA

The &4#'D&%!M&78 function provides another modeling method. More info on the

ARIMA model can be found here (https://en.wikipedia.org

/wiki/Autoregressive_integrated_moving_average). The &4#'D&%!M&78 function

automatically searches for the best model and optimizes the parameters. Usingthe &4#'D&%!M&78 is almost always better than calling the


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Great! These confidence intervals seem bit smaller than those for the ETSmodel. Maybe this is because of a better fit to the data, but let's train a third

model before doing a model comparison.

Model 3: TBATS

The last model I'm going to train is a TBATS model. This model is designed for

use when there are multiple cyclic patterns (e.g. daily, weekly and yearly

patterns) in a single time series. Maybe it will be able to detect complicated

patterns in our time series.

M(#N+ E #N+7#+(*&++$",$%+8

5(#N+ E 5'%$-&+#7M(#N+G 0ELJ8

*)'#75(#N+8

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Now we have three models that all seem to give reasonable predictions. Let'scompare them to see which is performing the best.

Model comparison

I'm going to use AIC (https://en.wikipedia.org

/wiki/Akaike_information_criterion) to compare the diff erent models. AIC is

common method for determining how well a model fits the data, while

penalizing more complex models. The model with the smallest AIC is the best

fitting model.

N&%*)'#7-7@OBEM($#+F&!-G


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We see that the ARIMA model performs the best. So, let's go ahead and turn ourinteractive R code into an R recipe that can be built into our DSS workflow.

But before we can do this, we have to turn the output of 5'%$-&+#78 into a

data.frame, so that we can store it in DSS.

First, I'm going to find the last date for which we have a measurement.

)&+#(.$ E !".$R7#+(*&++$",$%+8S)$",#07#+(*&++$",$%+8T

Then, I'm going to create data.frame with the prediction for each month. I'm

also going to include the lower and upper bounds of the predictions, and the

date. Since we're representing dates by the year, each month is 1/12 of a year.

5'%$-&+#(.5 E .&D5%&M$7*&++$",$%+(*%$.!-#$.E5(&&FM$&"G

*&++$",$%+()'U$%E5(&&F)'U$%SGLTG

*&++$",$%+(4**$%E5(&&F4**$%SGLTG

.$E)&+#(.$ V +$K7HWHLG LG N6EHWHL88

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Finally, we split the date column into separate columns for year and month.

5'%$-&+#(.5 E 5'%$-&+#(.5 XYX

M4#$76$&%E5)''%7.$88 XYX

M4#$7M'"#0E%'4".777.$ XX H8 Z HL8 V H88

All together the code is

)&+#(.$ E !".$R7#+(*&++$",$%+8S)$",#07#+(*&++$",$%+8T

5'%$-&+#(.5 E .&D5%&M$7*&++$",$%+(*%$.!-#$.E5(&&FM$&"G

*&++$",$%+()'U$%E5(&&F)'U$%SGLTG

*&++$",$%+(4**$%E5(&&F4**$%SGLTG

.$E)&+#(.$ V +$K7HWHLG LG N6EHWHL88

5'%$-&+#(.5 E 5'%$-&+#(.5 XYX

M4#$76$&%E5)''%7.$88 XYX

M4#$7M'"#0E77.$ XX H8 Z HL8 V H8

Awesome! Now we have everything we need to deploy the model onto DSS: the

code to create the forecast for the next 24 months and the code to convert the

result into a data.frame.

Deploying The Model In DSS

To deploy our model, we need to create a new R recipe. To do this, click on

!"#$%&"(&!%)!"$(*&++$",$%+(-)$&"$., then click on the R icon on the right, then

click on "Recipe Create new datasets using R code".

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I'm going to create a new managed dataset, 5'%$-&+#, for the output of my

recipe.

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That's it! Now we can click on run at the bottom of the page and return to the

DSS flow where we see our newly created forecast dataset.

Clicking on the 5'%$-&+# dataset let's us look at our new predictions stored as a

DSS dataset.

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If you thought this was helpful, check out our other tutorials

(http://learn.dataiku.com/).

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