-
A machine learning approach for forecasting hierarchical time series
In this paper, we propose a machine learning approach for forecasting hi...
read it
-
Hierarchical forecast reconciliation with machine learning
Hierarchical forecasting methods have been widely used to support aligne...
read it
-
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime
Reverse Vending Machines (RVMs) are a proven instrument for facilitating...
read it
-
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks
This paper discusses the prediction of hierarchical time series, where e...
read it
-
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective
Choosing the technique that is the best at forecasting your data, is a p...
read it
-
Déjà vu: forecasting with similarity
Accurate forecasts are vital for supporting the decisions of modern comp...
read it
-
Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview
In economics we often face a system, which intrinsically imposes a struc...
read it
Model selection in reconciling hierarchical time series
Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent. Although some hierarchical forecasting methods like minimum trace are strongly supported both theoretically and empirically for reconciling the base forecasts, there are still circumstances under which they might not produce the most accurate results, being outperformed by other methods. In this paper we propose an approach for dynamically selecting the most appropriate hierarchical forecasting method and succeeding better forecasting accuracy along with coherence. The approach, to be called conditional hierarchical forecasting, is based on Machine Learning classification methods and uses time series features as leading indicators for performing the selection for each hierarchy examined considering a variety of alternatives. Our results suggest that conditional hierarchical forecasting leads to significantly more accurate forecasts than standard approaches, especially at lower hierarchical levels.
READ FULL TEXT
Comments
There are no comments yet.