Advances in time series forecasting /:
Annotation
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Oak Park, Ill.] :
Bentham eBooks,
[2012]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation |
| Beschreibung: | 1 online resource (iii, 135 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and indexes. |
| ISBN: | 9781608053735 1608053733 |
Internformat
MARC
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| 245 | 0 | 0 | |a Advances in time series forecasting / |c editor, Cagdas Hakan Aladag ; co-editor, Erol Eǧrioǧlu. |
| 260 | |a [Oak Park, Ill.] : |b Bentham eBooks, |c [2012] | ||
| 300 | |a 1 online resource (iii, 135 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 504 | |a Includes bibliographical references and indexes. | ||
| 505 | 0 | |a pt. 1. Time series forecasting using ANN -- pt. 2. Fuzzy time series -- pt. 3. Hybrid methods. | |
| 520 | 8 | |a Annotation |b Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizational planning activities. Time series analysis is employed by many different organizations such as hospitals, universities, commercial enterprises or government organizations in order to forecast future scenarios. Therefore, many time series forecasting methods have been proposed and improved in statistical literature. Linear models such as Box-Jenkins methods were earlier used in many situations. Then, to overcome the restrictions of these linear models and to account for certain nonlinear patterns observed in real problems, some nonlinear models are also presented in literature. However, since these nonlinear models were developed for specific nonlinear patterns, they are not suitable for modeling other types of nonlinearity in time series. In recent years, efficient advanced techniques such as artificial neural networks, fuzzy time series and some hybrid models have been used to forecast any kind of real life time series. Both theoretical and empirical findings in academic literature show that these approaches give comparatively reliable forecasts than those obtained from conventional forecasting methods. In addition, conventional models require some assumptions such as linearity and normal distribution or cannot be utilized efficiently for some real time series such as temperature and share prices of stockholders since this kind of series contain some uncertainty in itself. However, when advanced methods such as neural networks and fuzzy time series are used to forecast time series, there is no need to satisfy any assumption and the time series contain uncertainty can be forecasted efficiently. This e-book contains recent effective applications and descriptions of these advanced forecasting methods. Readers will learn how these methods work and how these approaches can be used to forecast real life time series. In addition, the hybrid forecasting model approach, which combines different methods to obtain better forecast results, is also explained. Readers can also find the applications of hybrid forecasting models in this e-book. This e-book also enables skilled statisticians to create a new hybrid forecasting model suitable for their own objectives. Data presented in this e-book is problem based and is taken from real life situations. This e-book is a valuable resource for students, statisticians and working professionals interested in advanced time series analysis. | |
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
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| 650 | 7 | |a Time-series analysis |2 fast | |
| 700 | 1 | |a Aladag, Cagdas Hakan. | |
| 700 | 1 | |a Eǧrioǧlu, Erol. | |
| 758 | |i has work: |a Advances in Time Series Forecasting (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG4gtmb9JQpRhgWYY4pH83 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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Datensatz im Suchindex
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| _version_ | 1816882208915324929 |
| adam_text | |
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| author2 | Aladag, Cagdas Hakan Eǧrioǧlu, Erol |
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| author_facet | Aladag, Cagdas Hakan Eǧrioǧlu, Erol |
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| collection | ZDB-4-EBA |
| contents | pt. 1. Time series forecasting using ANN -- pt. 2. Fuzzy time series -- pt. 3. Hybrid methods. |
| ctrlnum | (OCoLC)811405863 |
| dewey-full | 519.5/5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/5 |
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| dewey-sort | 3519.5 15 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn811405863 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:24:57Z |
| institution | BVB |
| isbn | 9781608053735 1608053733 |
| language | English |
| oclc_num | 811405863 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (iii, 135 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Bentham eBooks, |
| record_format | marc |
| spelling | Advances in time series forecasting / editor, Cagdas Hakan Aladag ; co-editor, Erol Eǧrioǧlu. [Oak Park, Ill.] : Bentham eBooks, [2012] 1 online resource (iii, 135 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Includes bibliographical references and indexes. pt. 1. Time series forecasting using ANN -- pt. 2. Fuzzy time series -- pt. 3. Hybrid methods. Annotation Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizational planning activities. Time series analysis is employed by many different organizations such as hospitals, universities, commercial enterprises or government organizations in order to forecast future scenarios. Therefore, many time series forecasting methods have been proposed and improved in statistical literature. Linear models such as Box-Jenkins methods were earlier used in many situations. Then, to overcome the restrictions of these linear models and to account for certain nonlinear patterns observed in real problems, some nonlinear models are also presented in literature. However, since these nonlinear models were developed for specific nonlinear patterns, they are not suitable for modeling other types of nonlinearity in time series. In recent years, efficient advanced techniques such as artificial neural networks, fuzzy time series and some hybrid models have been used to forecast any kind of real life time series. Both theoretical and empirical findings in academic literature show that these approaches give comparatively reliable forecasts than those obtained from conventional forecasting methods. In addition, conventional models require some assumptions such as linearity and normal distribution or cannot be utilized efficiently for some real time series such as temperature and share prices of stockholders since this kind of series contain some uncertainty in itself. However, when advanced methods such as neural networks and fuzzy time series are used to forecast time series, there is no need to satisfy any assumption and the time series contain uncertainty can be forecasted efficiently. This e-book contains recent effective applications and descriptions of these advanced forecasting methods. Readers will learn how these methods work and how these approaches can be used to forecast real life time series. In addition, the hybrid forecasting model approach, which combines different methods to obtain better forecast results, is also explained. Readers can also find the applications of hybrid forecasting models in this e-book. This e-book also enables skilled statisticians to create a new hybrid forecasting model suitable for their own objectives. Data presented in this e-book is problem based and is taken from real life situations. This e-book is a valuable resource for students, statisticians and working professionals interested in advanced time series analysis. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Série chronologique. MATHEMATICS Probability & Statistics Time Series. bisacsh Time-series analysis fast Aladag, Cagdas Hakan. Eǧrioǧlu, Erol. has work: Advances in Time Series Forecasting (Text) https://id.oclc.org/worldcat/entity/E39PCG4gtmb9JQpRhgWYY4pH83 https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=500676 Volltext |
| spellingShingle | Advances in time series forecasting / pt. 1. Time series forecasting using ANN -- pt. 2. Fuzzy time series -- pt. 3. Hybrid methods. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Série chronologique. MATHEMATICS Probability & Statistics Time Series. bisacsh Time-series analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85135430 |
| title | Advances in time series forecasting / |
| title_auth | Advances in time series forecasting / |
| title_exact_search | Advances in time series forecasting / |
| title_full | Advances in time series forecasting / editor, Cagdas Hakan Aladag ; co-editor, Erol Eǧrioǧlu. |
| title_fullStr | Advances in time series forecasting / editor, Cagdas Hakan Aladag ; co-editor, Erol Eǧrioǧlu. |
| title_full_unstemmed | Advances in time series forecasting / editor, Cagdas Hakan Aladag ; co-editor, Erol Eǧrioǧlu. |
| title_short | Advances in time series forecasting / |
| title_sort | advances in time series forecasting |
| topic | Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Série chronologique. MATHEMATICS Probability & Statistics Time Series. bisacsh Time-series analysis fast |
| topic_facet | Time-series analysis. Série chronologique. MATHEMATICS Probability & Statistics Time Series. Time-series analysis |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=500676 |
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