InKyung Kim, DaeHee Kim, Jaekoo Lee

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 2, pp. 81-86, Feb. 2022
https://doi.org/10.3745/KTSDE.2022.11.2.81,   PDF Download:
Keywords: Time Series, Multi-variate Data Analysis, Multi-horizon Forecasting, Deep Learning, Neural Networks
Abstract

Given that time series are used in various fields, such as finance, IoT, and manufacturing, data analytical methods for accurate time-series forecasting can serve to increase operational efficiency. Among time-series analysis methods, multi-horizon forecasting provides a better understanding of data because it can extract meaningful statistics and other characteristics of the entire time-series. Furthermore, time-series data with exogenous information can be accurately predicted by using multi-horizon forecasting methods. However, traditional deep learning-based models for time-series do not account for the heterogeneity of inputs. We proposed an improved time-series predicting method, called the temporal fusion transformer method, which combines multi-horizon forecasting with interpretable insights into temporal dynamics. Various real-world data such as stock prices, fine dust concentrates and electricity consumption were considered in experiments. Experimental results showed that our temporal fusion transformer method has better time-series forecasting performance than existing models

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