Скачать или смотреть Supervised Learning covers Time Series(TS-1) data and provides the expected output.

It generally used to classify data or make predictions.
In supervised learning, data set is labeled data, which is classified, to infer a learning algorithm. The data set is used as the basis for predicting the classification of other unlabeled data through the use of machine learning algorithms.
Steps to follow
To solve a given problem of supervised learning, one has to perform the following steps:
1. Determine the type of training examples. Before doing anything else, the user should decide what kind of data is to be used as a training set.
2. Gather a training set. The training set needs to be representative of the real-world use of the function
3. Determine the input feature representation of the learned function. The accuracy of the learned function depends strongly on how the input object is represented.
4. Determine the structure of the learned function and corresponding learning algorithm.