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Chapter 8 Categorising What We Study and What We Analyse, and the Exercise of Interpretation (Book chapter)

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ISBN: 9783319768618 9783319768618 Year: Pages: 17 DOI: 10.1007/978-3-319-76861-8_8 Language: English
Publisher: Springer Grant: FP7 Ideas: European Research Council - 283601
Subject: History --- Migration
Added to DOAB on : 2018-07-02 11:01:01
License: Springer

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Abstract

A lot of qualitative researchers have a healthy wariness about straightforward categorisation
and modelling endeavours undertaken by quantitative researchers. Too
often, variables and measurements are too rigid in quantitative analysis to take stock
of all the complexity and context-dependency of human behaviour, attitudes and
identities. In the worst-case scenario for migration studies, this leads to oversimplification,
essentialisation and culturalism. In line with King et al. (1994), I would,
however, in this chapter, like to plead for qualitative researchers to take into account
that, in terms of challenges of validity and reliability, we have a lot to learn from
each other. Acknowledging that qualitative research has its distinctive advantages
(Brady and Collier 2004), I will argue that choices in categorisation, case selection
and research design are of crucial importance, perhaps even more in qualitative
studies than in quantitative studies, even if in both methodological traditions we are
confronted with similar challenges. Being transparent and reflecting on the consequences
of our choices of categorisation, analysis and interpretation is of crucial
importance. It is too easy to think that qualitative research would, by definition, be
better equipped in doing justice to the phenomena we wish to study in the field of
migration, especially if our research focusses on migrants.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

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