Exploratory Time Series Analysis

The authors at Knoyd have a post on exploratory data analysis of a time series data set:

From the plot above we can clearly see that time-series has strong seasonal and trend components. To estimate the trend component we can use a function from the pandas library called rolling_mean and plot the results. If we want to make the plot more fancy and reusable for another time-series it is a good idea to make a function. We can call this function plot_moving_average.

The second part of the series promises to use Box-Jenkins to forecast future values.

Related Posts

Kafka And The Differing Aims Of Data Professionals

Kai Waehner argues that there is an impedence mismatch between data engineers, data scientists, and ML production engineers: Data scientists love Python, period. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs. Both the stablest and most cutting edge APIs, as well as the majority of examples and tutorials use Python APIs. […]

Read More

Solving The Monty Hall Problem With R

Miroslav Rajter builds a Monty Hall problem simulator using R: The original and most simple scenario of the Monty Hall problem is this: You are in a prize contest and in front of you there are three doors (A, B and C). Behind one of the doors is a prize (Car), while behind others is […]

Read More

Categories

July 2018
MTWTFSS
« Jun Aug »
 1
2345678
9101112131415
16171819202122
23242526272829
3031