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.
Metadata
Item Type:
Article (Published)
Refereed:
Yes
Additional Information:
Article number: 101459
Uncontrolled Keywords:
Time series; Forecasting; Time Series Prediction; Diversity, Synthetic time series
Science Foundation Ireland under grant number SFI/12/RC/2289, Science Foundation Ireland (SFI) and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland under Grant Number 16/RC/3835.
ID Code:
26085
Deposited On:
03 Aug 2021 16:01 by
Fouad Bahrpeyma
. Last Modified 03 Aug 2021 16:01