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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Time Series Analysis Part 1 (intro)

A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.

Time Series Data obviously has to do with time and the first thing that comes to mind is finance. The world of finance is the world of time series, so stocks, and currency exchange rates and interest rates, they’re all Time series.

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Do I need a data science team?

One of the questions often addressed to me is whether an organization needs a data science team or not.

The way in which an organization will interact with data science depends a little bit on what kind of organization it is.

To some extent, it depends a lot on the size of the organization. So, when it is just a start up, when it is an early stage company, or just one person with a very small team, then we may not need to worry so much yet about how to do experimentation, how to do machine learning, how to do sort of prediction and downstream calculations. The first order of business is just making sure we keep our data in order. And the way to do that is to make sure we focus on infrastructure.

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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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