Introduction
Time series are the most common data to work with. Their main difference from any other data type is their dependence on time. The data change period can be anything: seconds, minutes, days, etc.
What a common dataset with time series looks like:
As you can see, the main difference between such a dataset and others is the presence of a datetime column responsible for the time indicator. In the example, a sample was written in the dataset exactly every hour.
Time series have many indicators we must analyze: seasonality, cyclicality, trends, stationarity, etc.
Based on these indicators, we can model the behavior of time series and predict their development.
In the following sections, you will get acquainted with time series processing algorithms and statistical models such as: autoregressive model, moving average, etc.
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Time series are the most common data to work with. Their main difference from any other data type is their dependence on time. The data change period can be anything: seconds, minutes, days, etc.
What a common dataset with time series looks like:
As you can see, the main difference between such a dataset and others is the presence of a datetime column responsible for the time indicator. In the example, a sample was written in the dataset exactly every hour.
Time series have many indicators we must analyze: seasonality, cyclicality, trends, stationarity, etc.
Based on these indicators, we can model the behavior of time series and predict their development.
In the following sections, you will get acquainted with time series processing algorithms and statistical models such as: autoregressive model, moving average, etc.
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