Essays about: "Hierarchical Forecasting"
Showing result 6 - 10 of 11 essays containing the words Hierarchical Forecasting.
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6. Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors : Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : When forecasting financial transaction volumes in different markets, different markets often exhibit similar seasonality patterns and public holiday behavior. In this thesis, an attempt is made at utilizing these similarities to improve forecasting accuracy as compared to forecasting each market individually. READ MORE
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7. Federated Learning in Large Scale Networks : Exploring Hierarchical Federated Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Federated learning faces a challenge when dealing with highly heterogeneous data and it can sometimes be inadequate to adopt an approach where a single model is trained for usage at all nodes in the network. Different approaches have been investigated to succumb this issue such as adapting the trained model to each node and clustering the nodes in the network and train a different model for each cluster where the data is less heterogeneous. READ MORE
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8. Forecasting High Yield Corporate Bond Industry Excess Return
University essay from KTH/Matematisk statistikAbstract : In this thesis, we apply unsupervised and supervised statistical learning methods on the high-yield corporate bond market with the goal of predicting its future excess return. We analyse the excess return of industry based indices of high-yield corporate bonds belonging to the Chemical, Metals, Paper, Building Materials, Packaging, Telecom, and Electric Utility industry. READ MORE
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9. Small Cohort Population Forecasting via Bayesian Learning
University essay from KTH/Matematisk statistikAbstract : A set of distributional assumptions regarding the demographic processes of birth, death, emigration and immigration have been assembled to form a probabilistic model framework of population dynamics. This framework was summarized as a Bayesian network and Bayesian inference techniques are exploited to infer the posterior distributions of the model parameters from observed data. READ MORE
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10. Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting
University essay from KTH/Matematisk statistikAbstract : In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. READ MORE
