Time Series Data Preparation
Francesca Lazzeri PhD · 2020
Time series data sets may have values that are missing or may contains outliers, hence the need for the data preparation and cleaning phase is essential. This chapter describes the most important steps to prepare the readers' time series data for forecasting models. Specifically, it discusses the following: Python for time series data; time series exploration and understanding; and time series feature engineering. The chapter looks at how Numpy, Matplotlib, and pandas can be very helpful for dealing with time series data. It focuses on these topics: how to get started with time series data analysis, how to calculate and review summary statistics for time series data, how to perform data cleaning of missing periods in time series, and how to perform time series data normalization and standardization. Feature engineering efforts mainly have two goals: creating the correct input data set to feed the machine learning algorithm and increasing the performance of machine learning models.