Explain MA modelling of time series?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

Explain MA modelling of time series?

Explain MA modelling of time series?

This recipe explains MA modelling of time series

0

Recipe Objective

The moving average (MA) method models the next step in the sequence as a linear function of the residual errors from a mean process at prior time steps. A moving average model is different from calculating the moving average of the time series.

So this recipe is a short example on what is MR modelling of time series. Let's get started.

Step 1 - Import the library

import numpy as np import pandas as pd from statsmodels.tsa.arima_model import ARMA

Let's pause and look at these imports. Numpy and pandas are general ones. Here statsmodels.tsa.arima_model is used to import ARMA library for building of model.

Step 2 - Setup the Data

df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date']) df.head()

Here, we have used one time series data from github.

Now our dataset is ready.

Step 3 - Splitting Data

train_data = df[1:len(df)-12] test_data = df[len(df)-12:]

Here we have simply split data into size of 12 and rest elements

Step 4 - Building MR model

model = ARMA(train_data.value, order=(0, 1)) model_fitted = model.fit()

We can use the ARMA class to create an MA model and setting a zeroth-order AR model. We must specify the order of the MA model in the order argument.

Step 5 - Printing the results

print('coefficients',model_fitted.params) predictions = model_fitted.predict(start=len(train_data), end=len(train_data) + len(test_data)-1) print(predictions)

Here, we have printed the coeffiecient of model and the predicted values.

Step 6 - Lets look at our dataset now

Once we run the above code snippet, we will see:

Scroll down the ipython file to visualize the output.

Relevant Projects

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Data Science Project on Wine Quality Prediction in R
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Solving Multiple Classification use cases Using H2O
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.