AI, Machine Learning, advanced analytics in the enterprise environment Part 2 (theory)

There is a lot of discussions the past years about the revolution predictive analytics will bring to the businesses. But, predictive analytics is not something new. It used to be a part of statistics! Corporations with the resources were doing it for decades. Nowadays, however, it is more accessible, due to technological advancements in the areas of computing power, software and storage, the abundance of big datasets, and advances in algorithm research. Any student can now use the cloud to perform an experiment that a few years ago would have been possible to be performed only by large corporations or government agencies.

The challenge is how can we apply advanced analytics, and AI in an enterprise environment, efficiently and effectively.

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AI, Machine Learning, advanced analytics in the enterprise environment Part 1 (a personal story from the 90s)

The first time I saw predictive analytics live, in praxis, to provide useful information was in the mid-90s. As a student and aspiring engineer, I was enrolled in a Total Quality/Six Sigma class. During a field trip we visited a Japanese run, car manufacturing plant where, to my astonishment, blue-collar workers were applying regression in order to predict when to change tools!

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Time Series Analysis Part 5 (All models are wrong)

“Come and join us, and we will show you how to predict the future”.

I tend to get invitations like this all time. Friendly people from marketing want to meet me and eventually sell tools or services to predict the future. So, it makes me wonder, if they actually have a machine to predict the future, why on earth are they spending their energy selling software and organizing events? Perhaps, you could pause for a second and consider what would you do if you had such a machine?

Although artificial intelligence has led to remarkable achievements in recent years, expectations for what the field is able and will be able to achieve in the next decade tend to run much higher than what will actually turn out to be possible. While some world-changing applications like autonomous cars are already within reach, many more are likely to remain elusive for a long time.

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Time Series Analysis Part 4 (Deep Learning)

We mentioned in the previous section that in the last few years, there have been many attempts at a fresh approach to statistical time-series forecasting using methods like neural networks and Deep Learning.

Machine Learning and Deep Learning are subsets of Artificial intelligence, as discussed here. Deep learning excels at identifying patterns in unstructured data, which most people know as media such as images, sound, video and text.

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Time Series Analysis Part 3 (Machine Learning)

We already have seen in the previous section how Time Series Modeling with the help of ARIMA can be realized.

In the last few years, there have been more attempts at a fresh approach to statistical time-series forecasting using the increasingly accessible tools of machine learning. This means methods like neural networks and extreme gradient boosting, as supplements or even replacements of the more traditional tools like auto-regressive integrated moving average (ARIMA) models.

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Time Series Analysis Part 2 (ARIMA)

‘Time’ is the most important factor which ensures success in a business. It’s difficult to keep up with the pace of time.  But, technology has developed some powerful methods to enable us to ‘see things’ ahead of time.  One such method, which deals with time-based data is Time Series Modeling. As the name suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making.

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