learn-co-students / dsc-2-26-11-section-recap-online-ds-pt-110419

Section Recap

Introduction

This short lesson summarizes the topics we covered in section 26 and why they'll be important to you as a data scientist.

Objectives

You will be able to:

• Understand and explain what was covered in this section
• Understand and explain why this section will help you become a data scientist

Key Takeaways

The key takeaways from this section include:

• A White Noise model has a fixed and constant mean and variance, and no correlation over time
• A Random Walk model has no specified mean or variance, but has a strong dependance over time
• The Pandas corr() function can be used to return the correlation between various time series data sets
• Autocorrelation allows us to identify how strongly each time serties observation is related to previous observations
• The autocorrelation function (ACF) is a function that represents autocorrelation of a time series as a function of the time lag
• The Partial Autocorrelation Function (or PACF) gives the partial correlation of a time series with its own lagged values, controlling for the values of the time series at all shorter lags
• ARMA (AutoRegressive and Moving Average) modeling is a tool for forecasting time series values by regressing the variable on its own lagged (past) values
• ARMA models assume that you've already detrended your data and that there is no seasonality
• ARIMA (Integrated ARMA) models allow for detrending as part of the modeling process and work well for data sets with trends but no seasonality
• SARIMA (Seasonal ARIMA) models allow for both detrending and seasonality as part of the modeling process
• Fracebook Prophet enables data analysts and developers alike to perform forecasting at scale in Python
• Prophet uses Additive Synthesis for time series forecasting