Time series Series with Power BI- Forecast with Arima-Part 12


In the last posts (Series of Time Series), I have explained about what is ARIMA, what is d,p, and q in ARIMA (p,d,q).

In this post, I will talk about how to use ARIMA for forecasting and how to handle the seasonality parameters.

in the last example for the age of death of the England king, we came up with an ARIMA model as below


now, we going to forecast based on ARIMA

1-first we need to create an ARIMA model as:


Arima model gets the time series object as first input, then the model parameters in a vector type.

we store the result in a variable name:


2- the second step is about forecasting. To do forecasting, we need a library name “forecast”. install it.


the forecast.Arima gets the model (the variable we sore ARIMA model in it) and for the second input, it gets the period we want to forecast. In this example, we want to know the forecast for the next 5 kings that in which age they will die.

Then we plot the forecast using “plot.forecast” function.

the whole code would be as below

kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)


the below forecast is shown here:


As you can see in the above picture, there is a slow decreasing trend in the forecast.

also you able to run this code in power query to see the numeric forecast instead of the chart.

so I have run the same code with a small change to it: create an output data frame to show data

# 'dataset' holds the input data for this script
kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)


so the below output will be as below:


the last question remains to answer is about the “intervals” low and high.

imaging the error (residual) in “21.55”

we are going to calculate the intervals for low and high range, for the high range we multiply it by 95% and for low, we multiply it by 80%. however, we have a table for mapping as below


the forecast is 67.8

so we have  67.8+ or -1.28(21.55) for 80% and for 95% we have 67.8+ or -1.96(21.55) which become

95 and 40,2 for 80%

110 and 25.56 for 95%

the time series for king age was none seasonal time series. in next post, I will show how to forecast and create an ARIMA model for seasonal TS.





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Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

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