Time series forecasting is a popular feature. Through the analysis of historical data, it is possible to predict future behavior of electricity demand in a given country.
Technologies Time series forecasting can be approached using different technologies, such as traditional models (moving average, exponential smoothing, and ARIMA model), machine learning (linear regression, logistic regression, random forests, neural networks, and gradient boosting), recurrent neural networks (LSTM or GRU), and convolutional neural networks (CNN). The choice of technology to use depends on the context and available data, so it is advisable to conduct a detailed analysis of the problem before selecting the appropriate technology.
Next Steps: Deploy the model and conduct further testing.