Research on PV Stock Prediction based on Time Series ARIMA Model

The photovoltaic (PV) building integration plate is new. To understand this plate's future development trend, the time prediction model and coefficient correlation analysis model are established to predict and analyze this plate. We used Python to preprocess the data, including comparing trading days for each stock and processing missing values. According to the data after analysis, we established the moving average model of photovoltaic building integration. In this paper, the time series auto regression integreate moving average (ARIMA) model predicts the plate index according to the previous data. The error is made between the expected results and the actual data. Finally, we improve the ESTABLISHED ARIMA model and use the improved model to predict the changing trend of the plate index two months later and draw the moving average line.