Time-Series Analysis With Box-Jenkins

The folks at Knoyd walk us through time series analysis using the Box-Jenkins method:

However, this approach is not generally recommended so we have to find something more appropriate. One option could be forecasting with the Box-Jenkins methodology. In this case, we will use the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model. In this model, we have to find optimal values for seven parameters:

  • Auto Regressive Component (p)
  • Integration Component (d)
  • Moving Average Component (q)
  • Seasonal Auto Regressive Component (P)
  • Seasonal Integration Component (D)
  • Seasonal Moving Average Component (Q)
  • Length of Season (s)

To set these parameters properly you need to have knowledge of auto-correlation functions and partial auto-correlation functions.

Read on for a nice overview of this method, as well as the importance of making sure your time series data set is stationary.

Related Posts

Interpreting The Area Under The Receiver Operating Characteristic Curve

Roos Colman explains what a Receiver Operating Characteristic (ROC) curve is and how we interpret the Area Under the Curve (AUC): The AUC can be defined as “The probability that a randomly selected case will have a higher test result than a randomly selected control”. Let’s use this definition to calculate and visualize the estimated […]

Read More

Naive Bayes Against Large Data Sets

Catherine Bernadorne walks us through using Naive Bayes for sentiment analysis: The more data that is used to train the classifier, the more accurate it will become over time. So if we continue to train it with actual results in 2017, then what it predicts in 2018 will be more accurate. Also, when Bayes gives […]

Read More

Categories

August 2018
MTWTFSS
« Jul Sep »
 12345
6789101112
13141516171819
20212223242526
2728293031