Fintech Market Price Prediction during Economic Crisis: Historical Trend Recognition with ARIMA Model

As financial markets face uncertainties and economic crises, the ability to predict market trends becomes crucial for informed decision-making. This study explores the application of the AutoRegressive Integrated Moving Average (ARIMA) model in predicting the market price of Fintech assets. Leveraging historical trends and recognizing the impact of economic crises, the ARIMA model is trained on data spanning from ۲۰۱۹ to ۲۰۲۱. By incorporating the anticipated return of a previous rising trend post-crisis, the model extends its predictions beyond the training period. The resistance level, indicative of the first rising trend, acts as a significant reference point. The resulting predictions are visualized alongside the original time series, revealing potential future market movements. The proposed methodology presents a novel approach to forecasting Fintech market prices, emphasizing the incorporation of historical trends and accounting for economic crises.

Keywords:

Fintech market prediction , ARIMA modeling , Economic crisis forecasting , Trend recognition