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by Vanderlei Munhoz Pereira Filho, Sanjeev Ghimire | Published February 8, 2019
ChicagoData scienceMachine learningObject Storage
In this code pattern, we’ll demonstrate how subject matter experts and data scientists can leverage IBM Watson Studio and Watson Machine Learning to automate data mining and the training of time series forecasters. This code pattern also applies Autoregressive Integrated Moving Average (ARIMA) algorithms and other advanced techniques to construct mathematical models capable of predicting trends based on data from the past.
Using IBM Watson Studio and Watson Machine Learning, this code pattern provides an example of data science workflow which attempts to predict the end-of-day value of S&P 500 stocks based on historical data. This pattern includes the data mining process that uses the Quandl API – a marketplace for financial, economic, and alternative data delivered in modern formats for today’s analysts.
After completing this code pattern, you’ll understand how to:
Find the detailed steps for this pattern in the readme file. The steps will show you how to:
What affects the price of Bitcoin? Use SPSS Statistics and data.world to do the analysis and find out.
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