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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Organizational Change Management

“The ability to learn faster than your competitors may be the only sustainable competitive advantage”
de Geus, HBR 1988

“The difficulty lies not so much in developing new ideas, as in escaping from the old ones.”. John Maynard Keynes

Change has become the essence of management … so to survive and prosper in the future you and your organization will have to perfect ‘outside-in’thinking skills.

An external consultant can help you manage change in an informed and systematic manner while better understanding developments in the environment and NOT as a series of crises.

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