Forecasting financial budget time series: ARIMA random walk vs LSTM neural network

Maryem Rhanoui, Siham Yousfi, Mounia Mikram, Hajar Merizak


Financial time series are volatile, non-stationary and non-linear data that are affected by external economic factors. There is several performant predictive approaches such as univariate ARIMA model and more recently Recurrent Neural Network. The accurate forecasting of budget data is a strategic and challenging task for an optimal management of resources, it requires the use of the most accurate model. We propose a predictive approach that uses and compares the Machine Learning ARIMA model and Deep Learning Recurrent LSTM model. The application and the comparative analysis show that the LSTM model outperforms the ARIMA model, mainly thanks to the LSTMs ability to learn non-linear relationship from data.


ARIMA; Deep learning; Financial time series; LSTM; Machine learning; Random walk; RNN

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