Monday, June 20, 2016

Predictive Analysis

Cloud9 Charts provides unprecedented abilities to perform Predictive Analytics on your datasets. The platform provides two options:
  • Out-of-the-box predictive analytics capabilities that tests a dataset against a variety of forecasting models to determine the model best suited to the data,  with the least Sum of Absolute Errors (SAE).  
  • Custom Predictive and Machine Learning algorithms that can be plugged along the data workflows.
This post focuses on the first option above that takes a hands-on look at how it works.  We'll take monthly stock prices for Amazon to determine predicted values over a three month period starting in July in a few simple steps. No signup is required to follow along.  

Models used include:
  • Simple Exponential, Double Exponential, Triple Exponential Smoothing Models
  • Moving Averages and Weighted Moving Averages
  • Naive Forecasting Model
  • Regression and Polynomial Regression Model
  • Multiple Linear Regression Model

The monthly stock prices looks like this:

Date, Price
06/01/16, 719.14
05/01/16, 683.85
04/01/16, 598.00
03/01/16, 579.00
02/01/16, 574.81
01/05/16, 633.79
12/01/15, 679.36
11/2/15, 628.35
10/1/15, 625.90
9/1/15, 511.89
8/3/15, 512.89
7/1/15, 536.15
6/1/15, 434.09
5/1/15, 429.23
4/1/15, 421.78
3/2/15, 372.10
2/2/15, 380.16
1/2/15, 354.53


1. Copy and paste the above dataset into

2. Click on Show me. The data will be parsed and visualized immediately. 

3. To perform predictions:
   i. Click on Analyze from the menu of the time series chart. This opens up an Analysis mode.
  ii. Drag date into the Grouping field. 
  iii. Click on 'Add a derived Field' option. Enter a name ("Predictions", for example) and in the operation, enter predict(price,date,07/01/2016,1m,3). This will choose the best model based on historical accuracy of the model to determine the projected prices over a three month period, on a monthly basis. 

That's it! In a few simple steps, you can apply predictive analytics on any of your own datasets. Enjoy!

Predictive Analysis docs:
Get Started: