Neural network structure identification in inflation forecasting

Abstract

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non-normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand-pull and cost-push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017.Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in-the-sample and outof-sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the 'best' NN because the optimal NN in-the-sample, based on MSE and/or AIC criteria, often has high out-ofsample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN.Moreover, JNN has a better forecasting performance than FNN.

Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?

“…Most previous studies have focused on inflation forecasting using ARIMA, STAR and VAR models, see, e.g., [7,[12][13][14]. Some of the studies have compared traditional econometric models against neural networks when forecasting inflation, see, e.g., [15][16][17][18], but only a few of them have dealt with the recurrent neural network as the competing one among other neural network structures, see, e.g., [10,19,20].…”

“…Ref. [10] showed that JNN has a significantly better forecasting performance one and six months ahead compared to the FNN. On the other hand, [18] compared machine learning models, including FNNs, with standard time-series models and concluded that multivariate models produce the most precise results in all horizons and that there is no single best model to forecast inflation.…”

“…In this paper, a single hidden layer Jordan neural network (JNN) is considered as the competing alternative which predicts inflation as good as professional forecasters. The main advantage of JNN over other neural networks is that it requires less hidden neurons in the hidden layer due to recurrent connections [10] and for the same recurrent structure it is suitable for forecasting nonstationary time-series with long memory [11]. In specific cases where JNN provides satisfactory results, an appropriate FNN counterpart requires more hidden neurons to achieve at least a similar performance.…”

Creating a Comprehensive Method for the Evaluation of a Company

“…Atsalakis [30] focuses specifically on the area of emission allowance prices, creating a model based on computational intelligence techniques for their prediction, including a hybrid neurofusion controller which forms a closed-loop feedback mechanism; an artificial neural network system (ANN) and an adaptive inference system (ANFIS). In their work, Šestanović and Arenrić [31] look for the optimal neural network for inflation prediction.…”

Forecasting inflation in Latin American countries using a SARIMA–LSTM combination

“…Conversely, Estiko and Wahyuddin (2019) found that ANN outperformed ARIMA when forecasting inflation in Indonesia. Finally, in a recent study forecasting inflation of the Euro, Sestanović and Arnerić (2021) found that the Jordan NN, outperformed a feedforward NN.…”