Blog
The latest news from Google AI
On the predictability of Search Trends
Monday, August 17, 2009
Posted by Yossi Matias, Niv Efron, and Yair Shimshoni, Google Labs, Israel.
Since
launching
Google Trends and Google Insights for Search, we've been providing daily insight into what the world is searching for. An understanding of search trends can be useful for advertisers, marketers, economists, scholars, and anyone else interested in knowing more about their world and what's currently top-of-mind.
As many have observed, the trends of some search queries are quite seasonal and have repeated patterns. See, for instance, the search trends for the query "
ski
" hit their peak during the winter seasons in the US and Australia. The search trends for
basketball
correlate with annual league events, and are consistent
year-over-year
. When looking at trends of the aggregated volume of search queries related to particular
categories
, one can also observe regular patterns in some categories like
Food & Drink
or
Automotive
. Such trends sequences appear quite predictable, and one would naturally expect the patterns of previous years to repeat looking forward.
On the other hand, for many other search queries and categories, the trends are quite irregular and hard to predict. Examples include the search trends for
obama
,
twitter
,
android
, or
global warming
, and the trend of aggregate searches in the
News & Current Events
category.
Having predictable trends for a search query or for a group of queries could have interesting ramifications. One could forecast the trends into the future, and use it as a "best guess" for various business decisions such as budget planning, marketing campaigns and resource allocations. One could identify deviation from such forecasting and identify new factors that are influencing the search volume as demonstrated in
Flu Trends
.
We were therefore interested in the following questions:
How many search queries have trends that are predictable?
Are some categories more predictable than others? How is the distribution of predictable trends between the various categories?
How predictable are the trends of aggregated search queries for different categories? Which categories are more predictable and which are less so?
To learn about the predictability of search trends, and so as to overcome our basic limitation of not knowing what the future will entail, we characterize the predictability of a Trends series based on its historical performance. In other words, we estimate the
a posteriori
predictability of a sequence determined by the error of forecasted trends vs the actual performance.
Specifically, we have used a simple forecasting model that learns basic seasonality and general trend. For each trends sequence of interest, we take a point in time,
t
, which is about a year back, compute a one year forecasting for
t
based on historical data available at time
t
, and compare it to the actual trends sequence that occurs since time
t
. The error between the forecasting trends and the actual trends characterizes the predictability level of a sequence, and when the error is smaller than a pre-defined threshold, we denote the trends query as
predictable
.
Our work to date is summarized in a paper called
On the Predictability of Search Trends
which includes the following observations:
Over half of the most popular Google search queries are predictable in a 12 month ahead forecast, with a mean absolute prediction error of about 12%.
Nearly half of the most popular queries are not predictable (with respect to the model we have used).
Some categories have particularly high fraction of predictable queries; for instance,
Health
(74%),
Food & Drink
(67%) and
Travel
(65%).
Some categories have particularly low fraction of predictable queries; for instance,
Entertainment
(35%) and
Social Networks & Online Communities
(27%).
The trends of aggregated queries per categories are much more predictable: 88% of the aggregated category search trends of over 600 categories in Insights for Search are predictable, with a mean absolute prediction error of of less than 6%.
There is a clear association between the existence of seasonality patterns and higher predictability, as well as an association between high levels of outliers and lower predictability. For the
Entertainment
category that has typically less seasonal search behavior as well as relatively higher number of singular spikes of interest, we have seen a predictability of 35%, where as the category of
Travel
with a very seasonal behavior and lower tendency for short spikes of interest had a predictability of 65%.
One should expect the actual search trends to deviate from forecast for many predictable queries, due to possible events and dynamic circumstances.
We show the forecasting vs actual for trends of a few categories, including some that were used recently for
predicting the present
of various economic indicators. This demonstrates how forecasting can serve as a good baseline for identifying interesting deviations in actual search traffic.
As we see that many of the search trends are predictable, we are introducing today a new
forecasting feature
in Insights for Search, along with a
new version
of the product. The forecasting feature is applied to queries which are identified as predictable (see, for instance,
basketball
or the trends in the
Automotive
category) and then shown as an extrapolation of the historical trends and search patterns.
There are many more questions that can be looked at regarding search trends in general, and their predictability in particular, including design and testing more advanced forecasting models, getting other insights into the distributions of sequences, and demonstrating interesting deviations of actual-vs-forecast for predictable trends series. We'd love to hear from you - share with us your findings, published results or insights - email us at insightsforsearch@google.com.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
