Introduction to time series
In a typical machine learning use case, a dataset is a collection of features (x) and target variables (y). The model uses features to learn and predict the target variable.
Take the following example. To predict house prices, the features could be the number of bedrooms, the number of baths, and square footage, and the target variable is the price of the house. Here, the goal can be to use all the features (x) to train the model and predict the price (y) of the house. One thing we observe in such a use case is that all the records in the dataset are treated equally when predicting target variables, which is the price of the house in our example, and the order of the data doesn't matter much. The outcome (y) depends only on the values of x.
On the other hand, in time series prediction, the order of the data plays an important role in capturing some of the features, such as trends and seasons. Time series datasets are typically datasets where...
