Hybrid Transformer and Holt-Winter's Method for Time Series Forecasting
Keywords: Transformer, Exponential Smoothing, Holt-Winter's Method
TL;DR: A hybrid model combining Exponential Smoothing and Transformers for time series forecasting
Abstract: Time series forecasting is an important research topic in machine learning due to its prevalence in social and scientific applications. Multi-model forecasting paradigm, including model hybridization and model combination, is shown to be more effective than single-model forecasting in the M4 competition. In this study, we hybridize exponential smoothing with transformer architecture to capture both levels and seasonal patterns while exploiting the complex non-linear trend in time series data. We show that our model can capture complex trends and seasonal patterns with moderately improvement in comparison to the state-of-the-arts result from the M4 competition.
Submission Number: 37
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