2021
DOI: 10.1007/s10614-021-10103-y
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New DTW Windows Type for Forward- and Backward-Lookingness Examination. Application for Inflation Expectation

Abstract: This study provides an application of dynamic time warping algorithm with a new window constraint to assess consumer expectations’ information content regarding current and future inflation. Our study’s contribution is the novel application of DTW for testing expectations’ forward-lookingness. Additionally, we modify the algorithm to adjust it for a specific question on the information content of our data. The DTW overcomes constraints of the standard tool that examines forward-lookingness: DTW does not impose… Show more

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Cited by 3 publications
(4 citation statements)
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References 35 publications
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“…The sequence is compared with the trained data in the database to determine whether it is the spiculation. The DTW distance of dynamic programming is introduced based on the scalability of time series to measure the degree of data similarity [27].…”
Section: Multidirection Projection Sign Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…The sequence is compared with the trained data in the database to determine whether it is the spiculation. The DTW distance of dynamic programming is introduced based on the scalability of time series to measure the degree of data similarity [27].…”
Section: Multidirection Projection Sign Analysismentioning
confidence: 99%
“…SVM [4] algorithm is adopted to build linear classifiers for classification. DTW [27] algorithm uses the sequence correlation to set thresholds to achieve classification. CNN [10] algorithm is adopted to build a convolutional neural network for developing the correlation between pixel value and target and realize classification.…”
Section: Multidirection Mip Algorithm Effectmentioning
confidence: 99%
“…However, the most common one is setting a window for warping, restricting the path to the diagonal region. Different structures of warping windows are proposed in [17] and dynamic time window strategy is presented in [18].…”
Section: Missing Value Treatmentmentioning
confidence: 99%
“…In this study, we used extension for this algorithm that is proposed in [25] to check if one time series is forward or backward against the other. We calculated separate DTW distances with windows proposed in [25], finding an optimal path only in the upper triangular cost matrix, within different but always forward shift (called further forward distance d f ), and in the lower triangular cost matrix, within backward shift (d b ).…”
Section: Dynamic Time Warpingmentioning
confidence: 99%