ZWPraedictio

This project aims to develop an artificial intelligence system that combines ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory) models to improve time series forecasting. The ARIMA model, based on statistical methods, is strong in capturing trends and seasonality in time series data but has limitations in handling complex patterns and non-linearity. On the other hand, LSTM, a type of artificial neural network, excels in learning non-linear patterns by considering long-term dependencies. This project seeks to combine the predictive capabilities of ARIMA with the learning capabilities of LSTM to achieve more accurate and reliable forecasting results across various time series data.

Please be aware that this is a private project and not available for public access. Access is restricted and requires appropriate permissions.