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 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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Artificial Intelligence, Machine Learning and Deep learning

A lot of people are using the terms Artificial Intelligence and Machine Learning interchangeably or to mean different things. This adds confusion to the complexity of modern technology.

When Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.

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Machine Learning (ML)

Machine Learning (ML) is a computing technique that has its origins in artificial intelligence (AI) and statistics. Machine Learning solutions include:

  • Classification– Predicting a Boolean true/false value for an entity with a given set of features.
  • Regression– Predicting a real numeric value for an entity with a given set of features.
  • Clustering– Grouping entities with similar features.
  • Recommendation– Recommending an item to a user based on past behavior or preferences of similar users.

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The Data Science Process_Part 2 (Practice)

As already discussed the Stages in the knowledge discovery process are:

  • Opportunity Assessment & Business Understanding
  • Data Understanding & Data Acquisition
  • Data Cleaning and Transformation
  • Model Building
  • Policy Construction
  • Evaluation, Residuals and Metrics
  • Model Deployment, Monitoring, Model Updates

Let’s see these stages in more detail:  Continue reading