Simple Predictive Analytics with NumPy | Learning NumPy Array

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Learning NumPy Array

By : Ivan Idris

4 (9)

Learning NumPy Array

4 (9)

By: Ivan Idris

Overview of this book

Learning NumPy Array

Learning NumPy Array

Credits

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Preface

Free Chapter

Getting Started with NumPy

Getting Started with NumPy

Python

Installing NumPy, Matplotlib, SciPy, and IPython on Windows

Installing NumPy, Matplotlib, SciPy, and IPython on Linux

Installing NumPy, Matplotlib, and SciPy on Mac OS X

Building from source

NumPy arrays

Online resources and help

Summary

NumPy Basics

NumPy Basics

The NumPy array object

Creating a multidimensional array

Selecting array elements

NumPy numerical types

Creating a record data type

One-dimensional slicing and indexing

Manipulating array shapes

Creating views and copies

Fancy indexing

Indexing with a list of locations

Indexing arrays with Booleans

Stride tricks for Sudoku

Broadcasting arrays

Summary

Basic Data Analysis with NumPy

Basic Data Analysis with NumPy

Introducing the dataset

Determining the daily temperature range

Looking for evidence of global warming

Comparing solar radiation versus temperature

Analyzing wind direction

Analyzing wind speed

Analyzing precipitation and sunshine duration

Analyzing monthly precipitation in De Bilt

Analyzing atmospheric pressure in De Bilt

Analyzing atmospheric humidity in De Bilt

Summary

Simple Predictive Analytics with NumPy

Simple Predictive Analytics with NumPy

Examining autocorrelation of average temperature with pandas

Describing data with pandas DataFrames

Correlating weather and stocks with pandas

Predicting temperature

Analyzing intra-year daily average temperatures

Introducing the day-of-the-year temperature model

Modeling temperature with the SciPy leastsq function

Day-of-year temperature take two

Moving-average temperature model with lag 1

The Autoregressive Moving Average temperature model

The time-dependent temperature mean adjusted autoregressive model

Outliers analysis of average De Bilt temperature

Using more robust statistics

Summary

Signal Processing Techniques

Signal Processing Techniques

Introducing the Sunspot data

Moving averages

Smoothing functions

Forecasting with an ARMA model

Filtering a signal

Demonstrating cointegration

Summary

Profiling, Debugging, and Testing

Profiling, Debugging, and Testing

Assert functions

Profiling a program with IPython

Debugging with IPython

Performing Unit tests

Nose tests decorators

Summary

The Scientific Python Ecosystem

The Scientific Python Ecosystem

Numerical integration

Interpolation

Using Cython with NumPy

Clustering stocks with scikit-learn

Detecting corners

Comparing NumPy to Blaze

Summary

Index

Index

Customer Reviews

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The Autoregressive Moving Average temperature model

The Autoregressive Moving Average (ARMA) model mixes the Autoregressive (AR) and Moving Average (MA) models. We have already discussed both models. Informally, we can say that we have the autoregressive component with white noise around it. Part of this white noise can be modeled as a linear combination of lag components plus some constant as follows:

Define an autoregressive model with lag 2 using linear coefficients we obtained with a previous script:
```
def ar(a):
   ar_p = [1.06517683, -0.08293789]
 
   return ar_p[0] * a[1:-1] + ar_p[1] * a[:-2]
```

Define the moving average model with lag 1:

def model(p, ma1):
   c0, c1 = p
 
   return c0 + c1 * ma1

Subtract the autoregressive model values from the data, giving us the error terms (white noise):
```
err_terms = temp[cutoff+1:] - ar(temp[cutoff-1:])
```
Most of the code for this model should appear familiar to you as shown in the following code:
```
import sys
import numpy as np
import matplotlib.pyplot as plt...
```

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