124,825 research outputs found
PREPROCESSING SEASONAL TIME SERIES FOR IMPROVING NEURAL NETWORK PREDICTIONS
No full textThe presence of stochastic or deterministic trends in economic time series can be a major obstacle for producing satisfactory predictions with neural networks. In this paper, we demonstrate the effects of nonstationarity on neural network predictions using the time series of the mortgage loans purchased in the Netherlands. We present different preprocessing techniques for removing nonstationarity, and evaluate their properties by producing multi-step predictions using a linear stochastic forecasting model and a neural network. The results indicate that detecting nonstationarity and selecting an appropriate preprocessing technique is highly beneficial for improving the prediction quality
Nonstationarity and Data Preprocessing for Neural Network Predictions of an Economic Time Series
No full textThe presence of stochastic or deterministic trends in economic time series can be a major obstacle for producing satisfactory predictions with neural networks. In this paper, we demonstrate the effects of nonstationarity on neural network predictions using the time series of the mortgage loans purchased in the Netherlands. We present different preprocessing techniques for removing nonstationarity, and evaluate their properties by producing multi-step predictions using a linear stochastic forecasting model and a neural network. The results indicate that detecting nonstationarity and selecting an appropriate preprocessing technique is highly beneficial for improving the prediction quality
AN APPLICATION OF DATA MINING METHODS TO AIRLINE OVERBOOKING OPTIMIZATION
No full textThis paper deals with the use of advanced statistics and data mining techniques to
extract knowledge from large databases containing passenger and booking information
(mainly the so called Passenger Name Records, PNR) of a major German airline company.
Such knowledge is used to predict passenger behavior, which in turn is used to optimize capacity planning and improve overbooking management. The preliminary results, obtained
on a sample of selected flights, show that it is possible to successfully use PNR data and appropriate models to improve the overbooking optimization process. Critical success factors
are: (a) data collection and preparation; (b) the method used for exploratory analysis and
data reduction; (c) the forecasting methods: complex methods performed better, but simple
methods might be preferred due to lower computational requirements and overall cost
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
ElasticHash: Semantic Image Similarity Search in Elasticsearch
No full textModels and datasets for publication: Korfhage, N., Mühling, M., Freisleben, B. (2021). ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_2 ElasticHash uses Elasticsearch for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearch (ES). In the first stage, a coarse search based on short hash codes is performed using multi-index hashing and ES terms lookup of neighboring hash codes. In the second stage, the list of results is re-ranked by computing the Hamming distance on long hash codes. The files published here are needed to set up the system for large-scale image similarity search and to reproduce the experiments. More details can be found in the Git-Repository: https://github.com/umr-ds/ElasticHas
Pragmatic Case Studies as a Source of Unity in Applied Psychology
No full textTo unify or not to unify applied psychology: that is the question. In this article we review pendulum swings in the historical efforts to answer this question—from a comprehensive, positivist, “top-down,” deductive yes between the 1930s and the early 60s, to a postmodern no since then. A rationale and proposal for a limited, “bottom-up,” inductive yes in applied psychology is then presented, employing a case-based paradigm that integrates both positivist and postmodern themes and components. This paradigm is labeled “pragmatic psychology” and, its specific use of case studies, the “Pragmatic Case Study Method” (“PCS Method”). We call for the creation of peer-reviewed journal-databases of pragmatic case studies as a foundational source of unifying applied knowledge in our discipline. As one example, the potential of the PCS Method for unifying different angles of theoretical regard is illustrated in an area of applied psychology, psychotherapy, via the case of Mrs. B. The article then turns to the broader historical and epistemological arguments for the unifying nature of the PCS Method in both applied and basic psychology.Peer reviewe
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