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Forecasting South Africa’s inflation rate using deep neural networks
Mini Dissertation (MSc eScience)--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613548865126400 |
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| access_status_str | Open Access |
| author2 | Van Eyden, Renee |
| author_browse | Van Eyden, Renee |
| author_facet | Van Eyden, Renee |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc eScience)--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/84274 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:54.123Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/84274 Forecasting South Africa’s inflation rate using deep neural networks Van Eyden, Renee kabothorisophage@gmail.com Phage, Kabo Thoriso UCTD Deep Neural Networks Inflation Forecasting Mini Dissertation (MSc eScience)--University of Pretoria, 2022. Inflation forecasting is crucial for efficient monetary policy and decision-making in an economy. This paper examines the feasibility of including deep neural networks in the macroeconomic forecasting toolbox for the South African economy. This study focuses on South Africa’s annual headline inflation rate and applies two different deep neural network architectures for forecasting. The deep neural network’s performance is compared to the autoregressive integrated moving average (ARIMA) benchmark, where root mean squared error (RMSE) is used as a performance measure. The results show that the multiple layer perceptron (MLP) outperformed the benchmark and its peer, the convolutional recurrent neural network model. Admittedly, the convolutional long-short term memory network (CNN-LSTM) is sensitive to architectural design, especially when the amount of training data is in short supply. In conclusion, the study finds that the ARIMA model predicts inflation inconsistently in the presence of endogenous and exogenous structural breaks in the time series and consequently gives non-unique forecasts. The MLP becomes a viable addition to the macroeconomic forecasting toolbox in such a case. DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP) Economics MSc eScience Unrestricted 2022-03-01T06:58:48Z 2022-03-01T06:58:48Z 2022 2022-01-14 Mini Dissertation * A2022 http://hdl.handle.net/2263/84274 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Deep Neural Networks Inflation Forecasting Forecasting South Africa’s inflation rate using deep neural networks |
| title | Forecasting South Africa’s inflation rate using deep neural networks |
| title_full | Forecasting South Africa’s inflation rate using deep neural networks |
| title_fullStr | Forecasting South Africa’s inflation rate using deep neural networks |
| title_full_unstemmed | Forecasting South Africa’s inflation rate using deep neural networks |
| title_short | Forecasting South Africa’s inflation rate using deep neural networks |
| title_sort | forecasting south africa s inflation rate using deep neural networks |
| topic | UCTD Deep Neural Networks Inflation Forecasting |
| url | http://hdl.handle.net/2263/84274 |