Explain MA modelling of time series?

Explain MA modelling of time series?

Explain MA modelling of time series?

This recipe explains MA modelling of time series


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.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

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.

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.

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

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 Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.