Explain AR modelling of time series?

This recipe explains AR modelling of time series

Recipe Objective

Autoregression modeling is a modeling technique used for time series data that assumes linear continuation of the series so that previous values in the time series can be used to predict futures values. It includes the idea of 'lag variables'.

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

Get Access to Time Series Analysis Real World Projects in Python

Step 1 - Import the library

import numpy as np import pandas as pd from statsmodels.tsa.ar_model import AR

Let's pause and look at these imports. Numpy and pandas are general ones. Here statsmodels.tsa.ar_model is used to import autorregressive 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['value'] = np.log(df['value']) df['value'] = df['value'].diff() df = df.drop(df.index[0]) df.head()

Here, we have used one time series data from github. Now, since this data is progressing and, we have normalized the set and taken difference so as to have a stationary series.

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 AR model

model = AR(train_data.value) model_fitted = model.fit()

We have simply build an AR model on our dataset and fit it.

Learn How Different Classification Techniques in Machine Learning Fair Against Each Other

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.

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

Build Piecewise and Spline Regression Models in Python
In this Regression Project, you will learn how to build a piecewise and spline regression model from scratch in Python to predict the points scored by a sports team.

Build Classification Algorithms for Digital Transformation[Banking]
Implement a machine learning approach using various classification techniques in Python to examine the digitalisation process of bank customers.

Time Series Python Project using Greykite and Neural Prophet
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

Topic modelling using Kmeans clustering to group customer reviews
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

End-to-End Speech Emotion Recognition Project using ANN
Speech Emotion Recognition using RAVDESS Audio Dataset - Build an Artificial Neural Network Model to Classify Audio Data into various Emotions like Sad, Happy, Angry, and Neutral

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

Build a Similar Images Finder with Python, Keras, and Tensorflow
Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.

Predictive Analytics Project for Working Capital Optimization
In this Predictive Analytics Project, you will build a model to accurately forecast the timing of customer and supplier payments for optimizing working capital.

BERT Text Classification using DistilBERT and ALBERT Models
This Project Explains how to perform Text Classification using ALBERT and DistilBERT