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Showing posts from 2018

Clouds, clouds, and more clouds

Clouds, clouds, and more clouds There are at least eleven kinds of clouds: cirrus, cirrocumulus, cirrostratus, altocumulus, altostratus, cumulonimbus, cumulus, nimbostratus, stratocumulus, small Cu, and stratus. But this article is not about those kinds of clouds. Of course there are other kinds of clouds, like iCloud, Google Cloud, Azure Cloud, Amazon Cloud, and the list goes on. But this article is not about those clouds either. This article is about text analytics.
Clouds and Text Analytics
The picture above was generated by R as a Word Cloud. It is equivalent to a frequency distribution (see Figure 1), where the size of the characters comprising a word corresponds to its frequency count, so that the word “icloud” occurs many times in the text, while the word “people” (inside the “d” in “icloud”) occurs less frequently. In Figure 1, “speed” occurs most frequently and is a key concept in flight dynamics, and in corresponding models.

Time Series Analysis using iPython

Time Series Analysis using iPython
In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) can be called from the tsa (Time Series) module from the Statamodels package.
Note: I am not as expert in time-series analysis as I am in other areas of Analytics, so if you find errors I would be happy to know about them and correct them. Introduction ARIMA models are, in theory, the most general class of models for forecasting a time series, which can be made to be “stationary” by differencing (if necessary), perhaps in conjunction with nonlinear transformations such as logging or deflating (if necessary). A random variable that is a time series is stationary if its statistical properties are all constant over time. A stationary series has no trend, its variations aro…