Artificial neural network model for inflation forecasting in Kenya
| dc.contributor.author | Mwangi, Carolyn Naomi Wanja | |
| dc.date.accessioned | 2016-10-07T16:08:05Z | |
| dc.date.available | 2016-10-07T16:08:05Z | |
| dc.date.issued | 2016 | |
| dc.description | Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Technology | en_US |
| dc.description.abstract | Forecasts are important in decision making and entail prediction of a future state of a particular subject of interest. These forecasts depend heavily on historical data and the assumption that the past behaviour of forecast inputs will replicate itself in the future. Current linear and macroeconomic theory forecasting models used in Kenya lack reliable accuracy when predictors are futuristic and subject to changes over time. Artificial Neural Network (ANN) allow for the model to be more versatile in incorporating new predictors without altering the structure of the model. They work exceptionally well in environments that are nonlinear and where data is noisy and sometimes unavailable. The structure for the proposed model is a Neural Network with Back Propagation learning algorithm incorporating rainfall and M-Pesa use effects as additional inflation variables. The Backpropagation Neural Network was selected as a useful alternative due to the non-linear data used and to facilitate forecasting of future values. The adaptability of ANNs makes them most suitable for dynamic forecasting and classification problems. The results obtained from the model indicated that the back propagation was an appropriate algorithm that can be implemented in the process of inflation forecasting. The forecasting was done based on inflation variables identified as true inputs to the process of inflation forecasting. The model accuracy performance at 71.4286 % showed that the model is reliable as a tool for inflation forecasting. The study found that the optimum learning rate for the model was 0.5 while the momentum was at 0.9 for the training and 0.7 for the testing and validation data. Total iterations varied between the train, test and validate phases. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/4828 | |
| dc.language.iso | en | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.subject | Neural network | en_US |
| dc.subject | Artificial Neural Network | en_US |
| dc.subject | Inflation | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | Artificial Inteligence | en_US |
| dc.title | Artificial neural network model for inflation forecasting in Kenya | en_US |
| dc.type | Thesis | en_US |