Friday Jun 03, 2016

Using Forecast Function in Oracle DV / OBIEE 12c

Oracle DV and OBIEE 12c offer a right click interaction to include forecast data in many visualizations.

But it also let users manually configure and edit Forecast functions as custom calculations. This Forecast function is based on a R script ran by Oracle BI Server, and comes with various options and parameters enabled. For example, it allows to define the number of periods to forecast, the type of forecasting model to use (Arima, ETS) and what specific parameters for this model (error type, seasonality, trending, Box Cox, Damping...) as well as what output data to produce (value, confidence levels high and low bounds). All these options combine in a range of various possibilities for calculating the most appropriate forecast information for the use case.

A free pre-built DV Destkop project showing various combinations of these options has been posted on the Oracle Technology Network Dataviz example page here (scroll to project example 'Forecast Syntax Examples') and this YT video gives a very brief introduction of how to use this function :

The FORECAST() Logical SQL Function takes a measure parameter e.g. revenue and a variable list of time dimensions. Optional column aliases can also be used. The default FORECAST XML (filerepo://obiee.TimeSeriesForecast.xml) script file on your server or laptop can be overridden by specifying a new one in the options string. 

Syntax : FORECAST( <measure_expr>, (<time_dimension_expr>), <column_name> , <options>, [<runtime_binded_options>])

- measure_expr represents the measure, e.g. revenue data to forecast.
- time_dimension_expr the time dimension to forecast. One or more columns may be provided.
- column_name represents the output column name for forecast.
- options is a string list of name/value pairs separated by ';'

Option Name



numPeriods the number of periods to forecast Integer
predictionInterval

the confidence for the prediction

Integer (1 to 99)

modelType

the model to use for forecasting

ARIMA, ETS

useBoxCox

if TRUE use box cox transformation

TRUE, FALSE

lambdaValue

the Box-Cox transformation parameter. Ignored if NULL or FALSE.

TRUE, FALSE
trendDamp

(ETS model). if TRUE, use damped trend, ie reduce effect of recent trends.

TRUE, FALSE
errorType

(ETS models) : controls how the nearest prior periods are weighted in the output

additive('A'), multiplicative('M'), automatic('Z')

trendType

(ETS models) : controls how the effect of trend is modeled in the output

None('N'), additive('A'), multiplicative('M'), automatic('Z')

seasonType

(ETS models) : controls how seasonal effects are affecting the model outputs.

None('N'), additive('A'), multiplicative('M'), automatic('Z')

modelParamIC Information criterion to be used in comparing and selecting different models and select the best model. 'ic_auto', 'ic_aicc', (corrected Akaike IC),'ic_bic‘ (Bayesian IC), 'ic_auto'(default)

- runtime_binded_options is an optional comma separated list of runtime binded colums and options.

Some Examples from the DVD Project :

Selecting prediction confidence interval, and showing low end and high end bounds

ETS vs ARIMA : what are the differences ?

Playing with ETS Trending and Seasonality parameters, what does it mean :

Find out more by downloading the example here (scroll to project example 'Forecast Syntax Examples').

Thank you.

Friday May 18, 2012

The Art of the Possible with Business Analytics

It has been established beyond doubt that data and its analysis can have a huge impact on an organization’s top line and bottom line. Business Analytics helps organizations deliver better business performance in two ways – by optimizing business processes and by helping to innovate. Optimization helps organizations be efficient and effective by taking inefficiencies out of the business processes and focusing on the high impact opportunities. Innovation on the other hand helps organizations by uncovering new customer segments, new product categories, new markets, new business models etc.

The styles of analyzing data are many fold from answering questions like “what is going on?” to “why are the things the way they are?” to “what will happen if I do X or Y?” to “what does the future look like?” Broadly speaking the styles of analytics can be classified into three categories:

·         Exploratory Analysis: The objective of exploratory or investigative analysis is exploration and analysis of complex and varied data – whether structured or unstructured for information discovery.  This style of analysis is particularly useful when the questions aren’t well formed or the value and shape of the data isn’t well understood.

·         Descriptive Analytics: The objective of this style of analysis is to answer historical or current questions like what is going on. why are the things the way they are?. This is the most common style of analysis and here the questions as well as the value and shape of data are well understood.

·         Predictive Analysis: Predictive analysis aims at painting a picture of the future with some reasonable certainty.

So, what’s art of possible with business analytics? It’s the application of the above three styles of analytics to a business scenario for better insights, decisions and results. Let’s try and explain this with an example. Consider this scenario:

You are a Financial Services firm e.g. a large bank and are trying to improve profitability. You read Larry Seldon’s book titled “Angel Customers and Demon Customers” and agree with the findings that 20% of your top customers bring in 80% of the profits and would like to manage you business as a portfolio of customers as opposed to portfolio of products. So, how do you do that? The answer is business analytics.

You can start by using descriptive analytics techniques like operational reports, ad-hoc query, dashboards etc. on data collected from different sources like sales, customer service etc. to determine the profitability of each customer. You can then use predictive analysis techniques like data mining, statistical analysis to further enrich your customer data into profitability segments like high, medium, low and loss making customers. Finally, you can choose different customer service channels like personal banker, phone or ATM to cost effectively serve you customers e.g. a high profitability customer can be served by a personal banker free of charge but if the loss making customer wants a personal banker there will be a charge. Once you have implemented such programs you can use exploratory analysis to gauge the sentiment across social media channels like Facebook and Twitter to see if the programs are working as desired. Better yet you may come up with new innovative business models like mobile banking or online only banking to improve profitability.

That’s the art of possible powered by business analytics. Stay tuned, I intend to publish more examples from different industries to show the art of possible with business analytics.
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