Data augmentation in economic time series: Behavior and improvements in predictions

The performance of neural networks and statistical models in time series prediction is conditioned by the amount of data available. The lack of observations is one of the main factors influencing the representativeness of the underlying patterns and trends. Using data augmentation techniques based on classical statistical techniques and neural networks, it is possible to generate additional observations and improve the accuracy of the predictions. The particular characteristics of economic time series make it necessary that data augmentation techniques do not significantly influence these characteristics, this fact would alter the quality of the details in the study. This paper analyzes the performance obtained by two data augmentation techniques applied to a time series and finally processed by an ARIMA model and a neural network model to make predictions. The results show a significant improvement in the predictions by the time series augmented by traditional interpolation techniques, obtaining a better fit and correlation with the original series.

Automated summary

The performance of neural networks and statistical models in time series prediction depends on the availability of data. Lack of observations affects the representativeness of patterns and trends. Data augmentation techniques can generate additional observations and improve prediction accuracy. This study analyzes the results of two data augmentation techniques applied to a time series and processed by an ARIMA model and a neural network model, showing significant improvement in predictions when using traditional interpolation techniques.