Abstract
In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing. However, for many researchers, the lack of time series presents a barrier to a deeper evaluation. While researchers have developed and used synthetic datasets, the development of this data requires a methodological approach to testing the entire dataset against a set of metrics which capture the diversity of the dataset. Unless researchers are confident that their test datasets display a broad set of time series characteristics, it may favor one type of predictive model over another. This can have the effect of undermining the evaluation of new predictive methods. In this paper, we present a new approach to generating and evaluating a high number of time series data. The construction algorithm and validation framework are described in detail, together with an analysis of the level of diversity present in the synthetic dataset.
Highlights
In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing
There are five metrics used to assess the time series components discussed in the previous section : Detrended Fluctuation Analysis (DFA), Spectral Entropy, Kurtosis, Skewness, and Gaussianity of the differenced values
The x-axis represents the range of values for a given feature and the y-axis shows the number of time series that fall into each specific interval
Summary
In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing. While researchers have developed and used synthetic datasets, the development of this data requires a methodological approach to testing the entire dataset against a set of metrics which capture the diversity of the dataset. Unless researchers are confident that their test datasets display a broad set of time series characteristics, it may favor one type of predictive model over another. This can have the effect of undermining the evaluation of new predictive methods. We present a new approach to generating and evaluating a high number of time series data.
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