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Μεταβολή της τιμής πολύτιμων και βιομηχανικών μετάλλων και η συσχέτισή τους με άλλους δείκτες

Kapsalis Ioannis-Anargyros

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URI: http://purl.tuc.gr/dl/dias/1BD29AA4-141C-46F9-953F-C2A7AFD5E1A4
Year 2023
Type of Item Diploma Work
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Bibliographic Citation Ιωάννης-Ανάργυρος Καψάλης, "Μεταβολή της τιμής πολύτιμων και βιομηχανικών μετάλλων και η συσχέτισή τους με άλλους δείκτες", Διπλωματική Εργασία, Σχολή Μηχανικών Ορυκτών Πόρων, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2023 https://doi.org/10.26233/heallink.tuc.96743
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Summary

This diploma thesis was prepared during the academic year 2021-2022. The purpose of the thesis is to investigate the factors that affect the performance of the prices of precious and industrial metals and their correlation with economic and environmental indicators. The analysis will be based on statistical methods to examine any correlations and whether independent variables can interpret the volatility in the price performance of precious and basic industrial metals.The following chapters will focus on carbon dioxide, metals in general, their history, and the correlations between them, mainly in Greece. We will discuss volatility of metal prices and focus on the real values of metals with data from the investing.com, for the purpose of temporal analysis of changes and predictions. A thorough reference is made to the theoretical part in order to understand basic concepts and categorize the time series for their further analysis. Tsaf (Time Series Analysis and Forecast) will be used, which enables us to analyze time series and Matlab toolbox. Specifically, we will utilize the forecasting models.1) Autoregressive model AR(p)2) moving average model MA(q)We will refer to the metals that interest us: copper, lead, nickel, tin, zinc, aluminum as well as the relationship between some of them with carbon dioxide. Data will be imported from excel showing the percentage of correlations of some metals with each other but also with carbon dioxide as well as figures that will show the correlation between the real values with the values predicted for the above models.Keywords Metals, Greece, Time series, Prediction and analysis of time series, Auto selfregression model AR(p), model of moving average MA(d), Partial autocorrelation, white noise, Trend, seasonality, Stationarity, Lags, autocorrelation, Pearson.

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