"Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input" - Information and Links:

Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input - Info and Reading Options


“Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” Metadata:

  • Title: ➤  Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input
  • Author: ➤  

“Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” Subjects and Themes:

Edition Identifiers:

  • Internet Archive ID: 10.11591eei.v10i5.2862

AI-generated Review of “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input”:


"Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input" Description:

The Internet Archive:

This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this automatic setting, users only need to supply the input of time series. Then, an automatic forecasting algorithm sets up the appropriate features, estimate the<br />parameters in the model, and calculate forecasts, without the users’ intervention. The algorithm method used include preprocessing, tests for trends, and the application of first differences. The time series were tested for seasonality, and seasonal differences were obtained from a successful analysis. These series were also linearly scaled to [−1, +1]. The autoregressive lags and hidden neurons were further selected through the stepwise and optimization algorithms, respectively. The 20 NARX models were fitted with different random starting weights, and the forecasts were combined using the ensemble operator, in order to obtain the final product. This proposed method was applied to real data, and its performance was compared with several available automatic<br />models in the literature. The forecasting accuracy was also measured by mean squared error (MSE) and mean absolute percent error (MAPE), and the results showed that the proposed method outperformed the other automatic models.

Read “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input”:

Read “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” by choosing from the options below.

Available Downloads for “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input”:

"Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 10.29 Mbs, and the file-s went public at Thu Nov 11 2021.

Legal and Safety Notes

Copyright Disclaimer and Liability Limitation:

A. Automated Content Display
The creation of this page is fully automated. All data, including text, images, and links, is displayed exactly as received from its original source, without any modification, alteration, or verification. We do not claim ownership of, nor assume any responsibility for, the accuracy or legality of this content.

B. Liability Disclaimer for External Content
The files provided below are solely the responsibility of their respective originators. We disclaim any and all liability, whether direct or indirect, for the content, accuracy, legality, or any other aspect of these files. By using this website, you acknowledge that we have no control over, nor endorse, the content hosted by external sources.

C. Inquiries and Disputes
For any inquiries, concerns, or issues related to the content displayed, including potential copyright claims, please contact the original source or provider of the files directly. We are not responsible for resolving any content-related disputes or claims of intellectual property infringement.

D. No Copyright Ownership
We do not claim ownership of any intellectual property contained in the files or data displayed on this website. All copyrights, trademarks, and other intellectual property rights remain the sole property of their respective owners. If you believe that content displayed on this website infringes upon your intellectual property rights, please contact the original content provider directly.

E. Fair Use Notice
Some content displayed on this website may fall under the "fair use" provisions of copyright law for purposes such as commentary, criticism, news reporting, research, or educational purposes. If you believe any content violates fair use guidelines, please reach out directly to the original source of the content for resolution.

Virus Scanning for Your Peace of Mind:

The files provided below have already been scanned for viruses by their original source. However, if you’d like to double-check before downloading, you can easily scan them yourself using the following steps:

How to scan a direct download link for viruses:

  • 1- Copy the direct link to the file you want to download (don’t open it yet).
    • (a free online tool) and paste the direct link into the provided field to start the scan.
  • 2- Visit VirusTotal (a free online tool) and paste the direct link into the provided field to start the scan.
  • 3- VirusTotal will scan the file using multiple antivirus vendors to detect any potential threats.
  • 4- Once the scan confirms the file is safe, you can proceed to download it with confidence and enjoy your content.

Available Downloads

  • Source: Internet Archive
  • Internet Archive Link: Archive.org page
  • All Files are Available: Yes
  • Number of Files: 15
  • Number of Available Files: 15
  • Added Date: 2021-11-11 03:30:58
  • Scanner: Internet Archive HTML5 Uploader 1.6.4
  • PPI (Pixels Per Inch): 300
  • OCR: tesseract 5.0.0-rc2-1-gf788
  • OCR Detected Language: en

Available Files:

1- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: 10.11591eei.v10i5.2862_files.xml
  • Direct Link: Click here

2- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: 10.11591eei.v10i5.2862_meta.sqlite
  • Direct Link: Click here

3- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: 10.11591eei.v10i5.2862_meta.xml
  • Direct Link: Click here

4- Text PDF

  • File origin: original
  • File Format: Text PDF
  • File Size: 0.00 Mbs
  • File Name: 55 2862.pdf
  • Direct Link: Click here

5- Item Tile

  • File origin: original
  • File Format: Item Tile
  • File Size: 0.00 Mbs
  • File Name: __ia_thumb.jpg
  • Direct Link: Click here

6- chOCR

  • File origin: derivative
  • File Format: chOCR
  • File Size: 0.00 Mbs
  • File Name: 55 2862_chocr.html.gz
  • Direct Link: Click here

7- DjVuTXT

  • File origin: derivative
  • File Format: DjVuTXT
  • File Size: 0.00 Mbs
  • File Name: 55 2862_djvu.txt
  • Direct Link: Click here

8- Djvu XML

  • File origin: derivative
  • File Format: Djvu XML
  • File Size: 0.00 Mbs
  • File Name: 55 2862_djvu.xml
  • Direct Link: Click here

9- hOCR

  • File origin: derivative
  • File Format: hOCR
  • File Size: 0.00 Mbs
  • File Name: 55 2862_hocr.html
  • Direct Link: Click here

10- OCR Page Index

  • File origin: derivative
  • File Format: OCR Page Index
  • File Size: 0.00 Mbs
  • File Name: 55 2862_hocr_pageindex.json.gz
  • Direct Link: Click here

11- OCR Search Text

  • File origin: derivative
  • File Format: OCR Search Text
  • File Size: 0.00 Mbs
  • File Name: 55 2862_hocr_searchtext.txt.gz
  • Direct Link: Click here

12- Single Page Processed JP2 ZIP

  • File origin: derivative
  • File Format: Single Page Processed JP2 ZIP
  • File Size: 0.01 Mbs
  • File Name: 55 2862_jp2.zip
  • Direct Link: Click here

13- Page Numbers JSON

  • File origin: derivative
  • File Format: Page Numbers JSON
  • File Size: 0.00 Mbs
  • File Name: 55 2862_page_numbers.json
  • Direct Link: Click here

14- Scandata

  • File origin: derivative
  • File Format: Scandata
  • File Size: 0.00 Mbs
  • File Name: 55 2862_scandata.xml
  • Direct Link: Click here

15- Archive BitTorrent

  • File origin: metadata
  • File Format: Archive BitTorrent
  • File Size: 0.00 Mbs
  • File Name: 10.11591eei.v10i5.2862_archive.torrent
  • Direct Link: Click here

Search for “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” downloads:

Visit our Downloads Search page to see if downloads are available.

Find “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” in Libraries Near You:

Read or borrow “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” from your local library.

Buy “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” online:

Shop for “Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input” on popular online marketplaces.



Find "Automatic Time Series Forecasting Using Nonlinear Autoregressive Neural Network Model With Exogenous Input" in Wikipdedia