The Proper Use of Google Trends in Forecasting Models
- URL: http://arxiv.org/abs/2104.03065v2
- Date: Thu, 8 Apr 2021 14:15:57 GMT
- Title: The Proper Use of Google Trends in Forecasting Models
- Authors: Marcelo C. Medeiros, Henrique F. Pires
- Abstract summary: Each sample of Google search data is different from the other, even if you set the same search term, data and location.
This means that it is possible to find arbitrary conclusions merely by chance.
This paper aims to show why and when it can become a problem and how to overcome this obstacle.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: It is widely known that Google Trends have become one of the most popular
free tools used by forecasters both in academics and in the private and public
sectors. There are many papers, from several different fields, concluding that
Google Trends improve forecasts' accuracy. However, what seems to be widely
unknown, is that each sample of Google search data is different from the other,
even if you set the same search term, data and location. This means that it is
possible to find arbitrary conclusions merely by chance. This paper aims to
show why and when it can become a problem and how to overcome this obstacle.
Related papers
- Unexpected Knowledge: Auditing Wikipedia and Grokipedia Search Recommendations [1.4323566945483497]
We provide the first comparative analysis of search engine in Wikipedia and Grokipedia.<n>We collect over 70,000 search engine results and examine their semantic alignment, overlap, and topical structure.<n>Our findings show that unexpected search engine outcomes are a common feature of both the platforms.
arXiv Detail & Related papers (2025-12-18T19:41:58Z) - Search Arena: Analyzing Search-Augmented LLMs [61.28673331156436]
We introduce Search Arena, a crowd-sourced, large-scale, human-preference dataset of over 24,000 paired multi-turn user interactions.<n>The dataset spans diverse intents and languages, and contains full system traces with around 12,000 human preference votes.<n>Our analysis reveals that user preferences are influenced by the number of citations, even when the cited content does not directly support the attributed claims.
arXiv Detail & Related papers (2025-06-05T17:59:26Z) - Auditing Google's Search Algorithm: Measuring News Diversity Across Brazil, the UK, and the US [0.0]
This study examines the influence of Google's search algorithm on news diversity by analyzing search results in Brazil, the UK, and the US.
It explores how Google's system preferentially favors a limited number of news outlets.
Findings indicate a slight leftward bias in search outcomes and a preference for popular, often national outlets.
arXiv Detail & Related papers (2024-10-31T11:49:16Z) - Limits to Predicting Online Speech Using Large Language Models [20.215414802169967]
Recent work suggests that the predictive information contained in posts written by a user's peers can surpass that of the user's own posts.
We collect a corpus of 6.25M posts from more than five thousand X (previously Twitter) users and their peers.
Across the board, we find that the predictability of social media posts remains low, comparable to predicting financial news without context.
arXiv Detail & Related papers (2024-07-08T09:50:49Z) - Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model [16.466711636334587]
Online decision-makers often obtain predictions on future variables, such as arrivals, demands, and so on.
Prediction accuracy is unknown to decision-makers a priori, hence blindly following the predictions can be harmful.
We develop algorithms that utilize predictions in a manner that is robust to the unknown prediction accuracy.
arXiv Detail & Related papers (2024-02-21T04:57:32Z) - Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining [75.25943383604266]
We question whether the use of large Web-scraped datasets should be viewed as differential-privacy-preserving.
We caution that publicizing these models pretrained on Web data as "private" could lead to harm and erode the public's trust in differential privacy as a meaningful definition of privacy.
We conclude by discussing potential paths forward for the field of private learning, as public pretraining becomes more popular and powerful.
arXiv Detail & Related papers (2022-12-13T10:41:12Z) - Domain Generalization -- A Causal Perspective [20.630396283221838]
Machine learning models have gained widespread success, from healthcare to personalized recommendations.
One of the preliminary assumptions of these models is the independent and identical distribution.
Since the models rely heavily on this assumption, they exhibit poor generalization capabilities.
arXiv Detail & Related papers (2022-09-30T01:56:49Z) - Assaying Out-Of-Distribution Generalization in Transfer Learning [103.57862972967273]
We take a unified view of previous work, highlighting message discrepancies that we address empirically.
We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting.
arXiv Detail & Related papers (2022-07-19T12:52:33Z) - The Matter of Chance: Auditing Web Search Results Related to the 2020
U.S. Presidential Primary Elections Across Six Search Engines [68.8204255655161]
We look at the text search results for "us elections", "donald trump", "joe biden" and "bernie sanders" queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex.
Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents.
arXiv Detail & Related papers (2021-05-03T11:18:19Z) - Domain Generalization: A Survey [146.68420112164577]
Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
For the first time, a comprehensive literature review is provided to summarize the ten-year development in DG.
arXiv Detail & Related papers (2021-03-03T16:12:22Z) - Google Trends Analysis of COVID-19 [3.1277175082738005]
The World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March.
Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus.
arXiv Detail & Related papers (2020-11-07T20:55:19Z) - Search Engine Similarity Analysis: A Combined Content and Rankings
Approach [6.69087470775851]
We present an analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo.
We developed a new similarity metric that leverages both the content and the ranking of search responses.
We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.
arXiv Detail & Related papers (2020-11-01T23:57:24Z) - Unification of HDP and LDA Models for Optimal Topic Clustering of
Subject Specific Question Banks [55.41644538483948]
An increase in the popularity of online courses would result in an increase in the number of course-related queries for academics.
In order to reduce the time spent on answering each individual question, clustering them is an ideal choice.
We use the Hierarchical Dirichlet Process to determine an optimal topic number input for our LDA model runs.
arXiv Detail & Related papers (2020-10-04T18:21:20Z) - Ambiguity in Sequential Data: Predicting Uncertain Futures with
Recurrent Models [110.82452096672182]
We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data.
We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
arXiv Detail & Related papers (2020-03-10T09:15:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.