How To Forecast Using Moving Averages For Time Series In R?

This recipe helps you forecast using moving averages for time series in R.

Objective For ‘How To Forecast Using Moving Averages For Time Series In R?’

This step-by-step recipe will show you how to forecast using moving averages for time series in R.

ProjectPro Free Projects on Big Data and Data Science

What Is The Moving Average Model For Time Series Forecasting?

Time series is a statistical technique that deals with time series data or trend analysis. Time series data means the data is collected over a period of time/ intervals.The time series moving average (MA) model is a popular technique used in time series forecasting that involves taking the average of a specified number of past observations to predict future values.  

Time series moving average formula- Moving Average = (Observation 1 + Observation 2 + ... + Observation N) / n

For example, suppose we have a time series dataset of monthly sales for the past 24 months. To use the time series moving average method, we would take the sales average for the past 'n' number of months (where 'n' is determined based on the data and the desired level of smoothing) to forecast the next month's sales.

So if we choose a moving average window of 3 months, the forecast for the next month's sales would be the average for the past 3 months. As new data becomes available, we can update the moving average forecast to make more accurate predictions.

Get Access to Time Series Analysis Real World Projects in Python

How To Forecast Using Moving Averages For Time Series In R?

Below is a time series moving average example to show you how moving average forecasting in R works for time series data.

Step 1 - Install required package

install.packages("zoo") # Install zoo package
library("zoo") # Load zoo

Step 2 - Generate random time series data

set.seed(1) # Creating example series
data <- 1:50 + rnorm(50, 0, 10)
data

Step 3 - Forecast using rollmean()

moving_avg <- rollmean(data, k = 5) # Apply rollmean function, here k = 5 means average is taken for 5 classes.
moving_avg
df = data.frame(moving_avg)
df

This example demonstrates using the 'zoo' package in R to forecast a time series using the rollmean() function. First, we need to install the 'zoo' package by running the install.packages("zoo") command. Then, we load the package by running library("zoo"). The code generates random time series data by setting a seed value with set.seed(1) and then creating a data vector of 50 numbers using the formula 1:50 + rnorm(50, 0, 10). This creates a data set that consists of the numbers 1 to 50 plus some random noise. Next, we apply the rollmean() function to the data using a window size of 5 (k = 5). The rollmean() function calculates the moving average of the data set over the specified window size. The result is stored in the variable 'moving_avg'. Finally, we convert the 'moving_avg' variable to a data frame using data.frame() and store it in the 'df' variable. This creates a data frame that shows the rolling average for the time series.

Gain expertise in big data tools and frameworks with exciting big data projects for students.

FAQs

You can find the average time series in R using the 'rollmean' function from the 'zoo' package. The function takes the time series data and a window size as input and returns the rolling average for the specified window size. For example, if you want to calculate the rolling average for a time series 'ts_data' with a window size of 5, you can use the following code: 'rollmean(ts_data, k = 5)'.

There is no "best" moving average for time series forecasting, as you must choose a method depending on the characteristics of the data and the purpose of the analysis. Simple moving averages are easy to compute and interpret, while exponential moving averages give more weight to recent observations. Other moving average methods, such as weighted and adaptive moving averages, may be ideal for certain data types. It's crucial to explore different methods and select the one that gives you the most accurate and meaningful forecasts.

 

Join Millions of Satisfied Developers and Enterprises to Maximize Your Productivity and ROI with ProjectPro - Read ProjectPro Reviews Now!

Access Solved Big Data and Data Science Projects

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

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.

Build an End-to-End AWS SageMaker Classification Model
MLOps on AWS SageMaker -Learn to Build an End-to-End Classification Model on SageMaker to predict a patient’s cause of death.

Learn Hyperparameter Tuning for Neural Networks with PyTorch
In this Deep Learning Project, you will learn how to optimally tune the hyperparameters (learning rate, epochs, dropout, early stopping) of a neural network model in PyTorch to improve model performance.

NLP and Deep Learning For Fake News Classification in Python
In this project you will use Python to implement various machine learning methods( RNN, LSTM, GRU) for fake news classification.

Learn How to Build a Linear Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple linear regression model in PyTorch to predict the number of days subscribed.

House Price Prediction Project using Machine Learning in Python
Use the Zillow Zestimate Dataset to build a machine learning model for house price prediction.

Recommender System Machine Learning Project for Beginners-1
Recommender System Machine Learning Project for Beginners - Learn how to design, implement and train a rule-based recommender system in Python

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

NLP Project to Build a Resume Parser in Python using Spacy
Use the popular Spacy NLP python library for OCR and text classification to build a Resume Parser in Python.

Build an AI Chatbot from Scratch using Keras Sequential Model
In this NLP Project, you will learn how to build an AI Chatbot from Scratch using Keras Sequential Model.