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Journals / Balkan Journal of Electrical and Computer Engineering / 2015 / Cilt: 3

Time Series Analysis and Data Relationships

Yunus BICEN
Mustafa KAYIKCI
Faruk ARAS
Pages
225–230
Publication Date
2015-12-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, it is tried to identify the main principles for determining the correct method and suitably selecting the parameters within the framework of time-series forecasting models and quantitative characteristics of the data sets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The time-series models which has an has an important place in the statistical forecasting methods are widely used in many disciplines such as economy, production management, and engineering in order to perform realistic estimates for the future.
📊
Key Findings & Data:Produced results of these methods which are diversified in time, is variable for different data sets.
💡
Conclusion & Impact:In this study, it is tried to identify the main principles for determining the correct method and suitably selecting the parameters within the framework of time-series forecasting models and quantitative characteristics of the data sets.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Exponential smoothing · forecasting methods · time series

Abstract

The time-series models which has an has an important place in the statistical forecasting methods are widely used in many disciplines such as economy, production management, and engineering in order to perform realistic estimates for the future. Produced results of these methods which are diversified in time, is variable for different data sets. A model that produces pretty good results for a dataset may not be realistic for the other dataset. The success of the time-series forecasting methods is directly related to the quantitative characteristic features of a dataset ranked through time. In this study, it is tried to identify the main principles for determining the correct method and suitably selecting the parameters within the framework of time-series forecasting models and quantitative characteristics of the data sets.

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Cite This Article

BICEN, Y., KAYIKCI, M., ARAS, F. (2015). Time Series Analysis and Data Relationships. Balkan Journal of Electrical and Computer Engineering, Cilt: 3, 225–230. https://ajindex.com/articles/473268

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