Name: Forecasting Studies: Comprehensive Review
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Forecasting Studies: Comprehensive Review

A Survey of 100 Papers from the Last 40 Years

By [source]

About this dataset

This dataset contains insightful data related to forecasting studies, highlighting the work of 100 influential papers from the last 40 years, hand-selected for their high citation levels and enduring importance. Through this valuable dataset, analyze more than just the publications year or average citations per year; data points like number of time series used, methods employed and measures performed are also tracked here. With this comprehensive review of forecasting studies in different domains, realize and appreciate the lasting impact these papers have had on this field

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How to use the dataset

This dataset offers an in-depth study on forecasting studies conducted over the past forty years. With it, you can easily survey the extensive literature surrounding forecasting and get an understanding of how issues within this field have evolved and developed over time.

To begin using this dataset, take a look at the columns included, and gain a better understanding of what they mean:

Name: The name of each paper in the dataset.

Url: The URL of each paper.

Cites Per Year: The average number of citations for each paper per year. This is an indication that these papers are important and influential within the field.

Year: The year each paper was published. This can help to understand how topics have changed throughout time with regards to forecasting practices and trends .

# Time Series: The number of time series used in each paper – useful if specific datasets are being surveyed across many publications or research topics need to be studied against particular datasets used over a period of time

# Methods & # Measures: These indicate which methods or measures were used by different researchers at different points in time to investigate various aspects related to forecasting studies – again helping with tracking changes across topics and areas related to forecasting

Once familiar with what information is contained within this dataset, users will be able to filter through data more efficiently depending on their research needs; allowing them access relevant results quickly without having to trawl through hundreds volumes worth of material!

Research Ideas

Analyzing the trends in time series forecasting methods over the years.

Identifying commonly-used time series and methodologies for forecasting different types of events, such as stock prices or industry trends.

Identifying new methods or measures which have yielded higher accuracy levels than existing ones for specific types of forecast studies

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reviewed articles.csv

Column name	Description
Name	The name of the paper. (String)
Url	The URL of the paper. (String)
Cites per year	The average number of citations per year for the paper. (Integer)
Year	The year the paper was published. (Integer)
# time series	The number of time series used in the paper. (Integer)
# methods	The number of methods used in the paper. (Integer)
# measures	The number of measures used in the paper. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Reviewed Articles

@kaggle.thedevastator_forecasting_studies_comprehensive_review.reviewed_articles

20.17 kB
100 rows
8 columns

CREATE TABLE reviewed_articles (
  "name" VARCHAR,
  "url" VARCHAR,
  "cites_per_year" BIGINT,
  "year" BIGINT,
  "cites" BIGINT,
  "n__time_series" BIGINT  -- # Time Series,
  "n__methods" BIGINT  -- # Methods,
  "n__measures" BIGINT  -- # Measures
);