How to forecast using moving averages for time series?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

# How to forecast using moving averages for time series?

This recipe helps you forecast using moving averages for 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. It is quite helpful for such such datset while making predictions.

So this recipe is a short example on how to predict using moving averages. 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.

## 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 moving average 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 - Making Predictions

``` predictions = model.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. Predict function simply let's us predicting train dataset.

## 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

##### German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

##### Predict Census Income using Deep Learning Models
In this project, we are going to work on Deep Learning using H2O to predict Census income.

##### Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

##### Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

##### Music Recommendation System Project using Python and R
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

##### Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

##### 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.

##### Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

##### 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.

##### Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.