Time series forecasting is a technique used in machine learning to predict future values based on previously observed values in a sequence. This kind of forecasting is widely used in various fields like finance, economics, weather prediction, and many other areas where time-dependent data are analyzed.

Here’s a general outline of how time series forecasting can be implemented using machine learning:

1. Data Collection and Preprocessing: The first step involves collecting and preprocessing the data. This includes handling missing values, scaling or normalizing the data, and possibly transforming it to make the series stationary.
2. Feature Engineering: Time-based features such as month, day of the week, hour, etc., can be extracted. Additionally, lag features (i.e., past values of the series) are commonly used to predict future values.
3. Splitting Data: The data is usually divided into training and testing sets to evaluate the model’s performance.
4. Model Selection: Various models can be used for time series forecasting, such as ARIMA, SARIMA, Exponential Smoothing State Space Models (ETS), Long Short-Term Memory (LSTM) networks, and more. The choice of the model depends on the nature of the data and the problem.
5. Model Training: The chosen model is trained on the training data using appropriate algorithms and hyperparameters.
6. Model Evaluation: Performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), etc., can be used to evaluate the model’s performance on the test set.
7. Forecasting: Finally, the trained model is used to predict future values of the time series.
8. Iterative Refinement: The model can be continually refined and retrained with new data to keep the predictions accurate and up-to-date.

It is also worth noting that when implementing time series forecasting, considerations must be made for seasonality, trends, and potential outliers in the data. Sophisticated preprocessing techniques and model selection can account for these factors.