Simple time series conversational engine

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Ten months ago I made a movie demonstrating a simple time series conversational engine. (I opened my “Quantile regression” presentation at WTC 2014 with that movie.)

Today I published a blog post that discusses the design and programming of the conversational engine: Simple time series conversational engine .

The conversational engine responds to commands for:

  1. loading time series data (weather data, stock data, or data files),
  2. finding outliers,
  3. analysis by curve fitting,
  4. plotting, and
  5. help and state changes.

A package with all of the code of the conversational engine can be downloaded from GitHub, [12] and executed with Import:

Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Examples/SimpleTimeSeriesConversationalEngine.m"]

SimpleTimeSeriesConversationalEngineExample.png
SimpleTimeSeriesConversationalEngineExample.png916×809 78.5 KB

More about functional parsers, which were essential for the building of the conversational engine, can be read in “Functional parsers” by Fokker.

Here are some commands to try:

  • load temperature for Atlanta

  • load stock price of NYSE:AAPL

  • find regression quantile for temperature of Atlanta

  • find outliers for temperature of Atlanta

  • least squares fit with Join[{1,x},Table[Sin[i*x/10^6.],{i,0,10}]]

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