DFT – Trigonometric representation
This issue focuses on using the DFT components to represent the input data set as the sum of the trigonometric sine-cosine functions.
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This issue focuses on using the DFT components to represent the input data set as the sum of the trigonometric sine-cosine functions.
In this entry, we examine the Discrete Fourier Transform (DFT) and its inverse, as well as data filtering using DFT outputs.
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In this study, we examine the daily prices of the first four (4) contracts of WTI CL futures listed on NYMEX, compute the number of days to the delivery month for each contract, and carry out principal component analysis (PCA) in an attempt to uncover the core drivers behind the futures curve changes (i.e. level and general shape).
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In this case, we examine the highway retail price ($/Gallon) for “No.2 Ultra Low Sulfur (0-15 ppm) Diesel” in the EIA nine (9) PADD regions and carry on principal component analysis in an attempt to find a minimal subset of the principal components that capture (or explains) the variation (spreads) in prices across different regions with a minimal loss of information.
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