A machine learning approach for forecasting hierarchical time series
- URL: http://arxiv.org/abs/2006.00630v2
- Date: Mon, 3 May 2021 14:13:07 GMT
- Title: A machine learning approach for forecasting hierarchical time series
- Authors: Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso
- Abstract summary: We propose a machine learning approach for forecasting hierarchical time series.
Forecast reconciliation is the process of adjusting forecasts to make them coherent across the hierarchy.
We exploit the ability of a deep neural network to extract information capturing the structure of the hierarchy.
- Score: 4.157415305926584
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a machine learning approach for forecasting
hierarchical time series. When dealing with hierarchical time series, apart
from generating accurate forecasts, one needs to select a suitable method for
producing reconciled forecasts. Forecast reconciliation is the process of
adjusting forecasts to make them coherent across the hierarchy. In literature,
coherence is often enforced by using a post-processing technique on the base
forecasts produced by suitable time series forecasting methods. On the
contrary, our idea is to use a deep neural network to directly produce accurate
and reconciled forecasts. We exploit the ability of a deep neural network to
extract information capturing the structure of the hierarchy. We impose the
reconciliation at training time by minimizing a customized loss function. In
many practical applications, besides time series data, hierarchical time series
include explanatory variables that are beneficial for increasing the
forecasting accuracy. Exploiting this further information, our approach links
the relationship between time series features extracted at any level of the
hierarchy and the explanatory variables into an end-to-end neural network
providing accurate and reconciled point forecasts. The effectiveness of the
approach is validated on three real-world datasets, where our method
outperforms state-of-the-art competitors in hierarchical forecasting.
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