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Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 11))

Abstract

In the previous chapter, we saw how to predict the future prices of an asset, based on its past prices. This problem was called “time series prediction”, and there are many different techniques, both traditional and evolutionary, to perform this task. All these techniques use the information provided by the past prices of the stock, called the historical data, to forecast the future price.

However, sometimes to make a financial decision, we don’t need to know the exact price of the asset in the future. For some policies, knowing only that the asset’s price rise or fall in the short term may be enough to decide whether to hold a position or to close it.

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Correspondence to Hitoshi Iba .

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© 2012 Springer Berlin Heidelberg

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Iba, H., Aranha, C.C. (2012). Trend Analysis. In: Practical Applications of Evolutionary Computation to Financial Engineering. Adaptation, Learning, and Optimization, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27648-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-27648-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27647-7

  • Online ISBN: 978-3-642-27648-4

  • eBook Packages: EngineeringEngineering (R0)

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