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Showing 1 to 4 of 4 for “"Hierarchical time series"”.
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Coherency Loss for Hierarchical Time Series Forecasting
In hierarchical time series forecasting, some series are aggregated from others, producing a known coherency metric between series. We present a new method for enforcing coherency on hierarchical time series forecasts. We propose a new loss function, called Network Coherency Loss, that minimizes …
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Essays in Hierarchical Time Series Forecasting and Forecast Combination
… Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using monthly data that underlies the Retail Prices Index for the UK, we analyse the dynamics of the inflation …
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Hierarchical forecasting of electricity demand in South Africa
The study focuses on the application of hierarchical time series in forecasting electricity demand using South African data. The methods used are top-down, bottom-up and optimal combination. The top-down method is based on the disaggregation of the forecasts of the total series and distribute these …
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Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making
… reduces the initial hard-to-solve problem into a series of significantly smaller, easier-to-solve problems. We further extend this framework to any differentiable neural network or MIP-expressible machine learning model. In Chapter 4, we focus on structured machine learning. We first address the …