Fr. 57.50

STOCK PRICE PREDICTION USING TIME SERIES - DE

English · Paperback / Softback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

The ARIMA model and the EXPONENTIAL SMOOTHING model for stock price prediction were given in this book. Each algorithm identifies the stock data set of all five institutions, according to the evaluations of these two models. The ARIMA model test results showed that it can reliably predict stock prices in the short term. This can lead to beneficial investment decisions for stock market speculators. The ARIMA model may be ready to compete with other short-term prediction models based on the findings obtained. A wide range of frequency values can be used using exponential smoothing. The Exponential smoothing approach was chosen for a single time series that followed a pattern in terms of order selection. There are many well-known time series techniques in the ARIMA. The design section of ARIMA was critical, delivering a nearly straight line.

About the author










Il Dr. K Sateesh Kumar lavora come professore assistente presso lo SNIST di Hyderabad. Le sue aree di interesse sono l'elaborazione digitale delle immagini, il telerilevamento e l'apprendimento automatico, ecc. Ha diverse pubblicazioni internazionali in riviste e conferenze rinomate.

Product details

Authors Kanagala Sateesh Kumar
Publisher LAP Lambert Academic Publishing
 
Languages English
Product format Paperback / Softback
Released 05.09.2023
 
EAN 9786206781806
ISBN 9786206781806
No. of pages 68
Subject Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.