TY  - BOOK
AU  - Mwangi, Carolyn N.W.
AU  - Lukandu Ismail Ateya (Prof.)
TI  - Artificial neutral network model for inflation forecasting in Kenya
AV  - HB3730.M836 2016 
PY  - 2016///
CY  - Nairobi
PB  - Strathmore University
KW  - Inflation--Forecasting--Kenya
KW  - Inflation--Forecasting--Artificial Neural network
N2  - 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
UR  - https://su-plus.strathmore.edu.ezproxy.library.strathmore.edu/handle/11071/4828
ER  -