Sale!

Multivariate Approach to Time Series Model Identification

3,000.00

If you are interested in getting this project material “Multivariate Approach to Time Series Model Identification”, click on the DOWNLOAD BUTTON to make payment and the file will be delivered to your email immediately after confirmation.

Description

– Multivariate Approach to Time Series Model Identification –

Download Multivariate Approach to Time Series Model Identification. Statistics students who are writing their projects can get this material to aid their research work.

Abstract

This work suggests an exact and systematic model identification approach which is entirely new and addresses most of the challenges of existing methods. We developed quadratic discriminant functions for various orders of autoregressive moving average (ARMA) models.

An Algorithm that is to be used alongside our functions was also developed. In achieving this, three hundred sets of time series data were simulated for the development of our functions.

Another twenty five sets of simulated time series data were used in testing out the classifiers which correctly classified twenty three out of the twenty five sets.

The two cases of misclassification merely imply that our Algorithm will require a second iteration to correctly identify the model in question. The Algorithm was also applied to some real life time series data and it correctly classified it in two iterations

Introduction

Model identification is a crucial part of Time Series model development. The main task of Time Series Modeling is to first examine the series at hand so as to  establish the theoretical model that generates the Series.

This task seems to be the most challenging and most ambiguous in Time Series Modeling. It has been approached from different perspectives over time. One of the most popular approach is the Box and Jenkins approach presented in Box and Jenkins (1976).

Their method involves going through some iterative steps before a final model is selected. The initial step involves calculating the sample autocorrelation function (ACF) and partial autocorrelation function (PACF) of the series at various lags and comparing their behaviour with known behaviour of some theoretical model and the model that best approximates the sample behavior is tentatively selected.

There are two serious problems with their method. First is the fact that one will need to fit several models or do several adjustments to arrive at the final model.

This makes the method computationally expensive. Another serious problem is the inability of the method to accurately differentiate between some classes of models.

How to Download this Project Material

First, note that we are one of the best and most reliable online platforms because we don’t retain any of your personal information or data as regards making payments online.

PRICE: ₦3,500 ₦3,000  (Three Thousand Naira Only)

Make a bank deposit or mobile transfer of ₦2,000 only to the account given below;


Bank Name: UBA Account Number: 1022564031 Account Name: TMLT PRO SERVICES


After making the payment, CLICK HERE to send the following on WhatsApp;

  • Depositor’s Name or Screenshot of Payment
  • Name of the Past Question
  • Active Email Address

or Call Us On +2348082284439 Once your details have been received and your payment confirmed by us, you will receive the past question in your email or WhatsApp within 5 Minutes.

Guarantee of Getting the Material 

We understand that due to the high rate of fraud, many people are afraid of making purchases online but be rest assured that PastExamQuestions will deliver your material after payment.

Once your details have been received and your payment confirmed by us, you will receive the past question in your email or WhatsApp.

Give us Feedback

Have we been able to satisfy you? How well do you think the material will be helpful after having gone through it? Does the price worth the material?

Let’s hear from you! We recommend that our customers give feedback at the end of every transaction to enable us to serve better. You can do this by clicking the review button on this page.

Where is the review button? >> Just scroll up to where you see reviews

Reviews

There are no reviews yet.

Be the first to review “Multivariate Approach to Time Series Model Identification”

Your email address will not be published. Required fields are marked *