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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