Time Series in Python Part 1: Read and Transform Your Data
In part 1 of this video series, learn how to read and index your data for time series using Python’s pandas package. We check if the data meets the requirements or assumptions for time series modeling by plotting to see if it follows a stationary pattern. We also transform our data by taking differences in the values to make them more stationary. This series is considered for intermediate and advanced users. Please watch our video on getting python setup for data science. Check out our data science bootcamp if you are a beginner looking to jump start your data science journey.
Code, R & Python Script Repository
Watch Part 2 Here:
ARIMA modeling and Forcasting: Time Series in Python
Watch Part 3 Here:
Mean Absolute Error for Forecast Evaluation: Time Series in Python
Packages Used:
pandas
matplotlib
StatsModels
statistics
More Data Science Material:
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[Video] Web scraping in R using rvest
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