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Erschienen in:
Technical Analysis Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

2. Preprocessing Procedures

verfasst von : Prodromos E. Tsinaslanidis, Achilleas D. Zapranis

Erschienen in: Technical Analysis for Algorithmic Pattern Recognition

Verlag: Springer International Publishing

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Abstract

The purpose of this chapter is to present two important preprocessing procedures than need to be carried before someone moves to the phase of recognizing technical patterns in financial price series. Initially, the importance of detecting errors in a dataset and various manners of replacing missing values are discussed.
This chapter further describes the algorithms for the identification of regional locals and perceptually important points. These algorithms are a common prerequisite step for all the proposed, patterns’ identification mechanisms presented in this book.

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Vorheriges Kapitel Technical Analysis
Nächstes Kapitel Assessing the Predictive Performance of Technical Analysis
Anhänge
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Fußnoten
1
Autoregressive Integrated Moving Average.
 
2
Functions presented in this book have the following general form: [output1, output2, …, outputn] = function’s name (input1, input2, …, inputn). The variables in the squared brackets are the outputs generated by the corresponding function and the variables inside the brackets are the necessary inputs. We follow the same notation used in the Matlab software since all the identification mechanisms presented in this book were developed with the use of this software.
 
3
Hereafter we will use the notation [a : b] to refer to all positive natural values between the closed interval [a, b], where \( 0<a<b \) and \( a,b\in\ \mathrm{\mathbb{N}} \).
 
4
A comprehensive review on the existing time series data mining research is presented in Fu (2011), where variant methodologies that deal with the aforementioned aspects of data mining are highlighted.
 
5
Since the first two PIPs are defined as the first and the last observation the ı th iteration identifies the \( {\left(\imath +2\right)}^{th} \) PIP.
 
6
nan stands for “not-a-number”. A nan value is the result from operations which have undefined numerical results. When nan is involved in a calculation (for example \( nan\times 10 \)) the result is also nan.
 
7
Since the division between matrices is not defined in Eqs. (2.13) and (2.14) an element wise division is implied.
 
Literatur
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Chung FL, Fu TC, Luk R, Ng V (2001) Flexible time series pattern matching based on perceptually important points. Paper presented at the international joint conference on artificial intelligence workshop on learning from temporal and spatial data
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Fu TC (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181CrossRef
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Metadaten
Titel
Preprocessing Procedures
verfasst von
Prodromos E. Tsinaslanidis
Achilleas D. Zapranis
Copyright-Jahr
2016
Buch
Technical Analysis for Algorithmic Pattern Recognition

Print ISBN: 978-3-319-23635-3

Electronic ISBN: 978-3-319-23636-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-23636-0_2