First, preprocess the time series data by transforming it into a stationary format. This can be done by removing trend and seasonality, typically through techniques like differencing or decomposition.

Next, select a model architecture, such as ARIMA or LSTM, that is suitable for the specific characteristics of the time series data.

Train the model on historical data and evaluate its performance using metrics such as mean absolute error or root mean squared error.

Finally, use the trained model to make predictions on new, unseen data.

It's also a good idea to check if the residuals of the model are white noise, which means the model is good fit for the time series data.