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Article overview
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Dominant Dataset Selection Algorithms for Time-Series Data Based on Linear Transformation | Yi Wu
; Yi Liu
; Jialiang Peng
; | Date: |
1 Mar 2019 | Abstract: | With the explosive growth of time-series data, the scale of time-series data
has already exceeds the conventional computation and storage capabilities in
many applications. On the other hand, the information carried by time-series
data has high redundancy due to the strong correlation between time-series
data. In this paper, we propose the new dominant dataset selection algorithms
to extract the dataset that is only a small dataset but can represent the
kernel information carried by time-series data with the error rate less than
{epsilon}, where {epsilon} can be arbitrarily small. We prove that the
selection problem of the dominant dataset is an NP-complete problem. The affine
transformation model is introduced to define the linear transformation function
to ensure the selection function of dominant dataset with the constant time
complexity O(1). Furthermore, the scanning selection algorithm with the time
complexity O(n2) and the greedy selection algorithm with the time complexity
O(n3) are respectively proposed to extract the dominant dataset based on the
linear correlation between time-series data. The proposed algorithms are
evaluated on the real electric power consumption data of a city in China. The
experimental results show that the proposed algorithms not only reduce the size
of kernel dataset but ensure the time-series data integrity in term of accuracy
and efficiency | Source: | arXiv, 1903.0237 | Services: | Forum | Review | PDF | Favorites |
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