Editing Time series (section)

===Exploratory analysis===
{{Further|Exploratory analysis}}

[[File:Time series TB US.png|thumb|Time series of tuberculosis deaths in the United States 1954-2021.]]

A simple way to examine a regular time series is manually with a [[line chart]]. The datagraphic shows tuberculosis deaths in the United States,<ref>{{cite web | url=http://www.cdc.gov/tb/statistics/reports/2022/table1.htm | title=Table 1 &#124; Reported TB in the US 2022&#124; Data & Statistics &#124; TB &#124; CDC | date=27 August 2024 }}</ref> along with the yearly change and the percentage change from year to year. The total number of deaths declined in every year until the mid-1980s, after which there were occasional increases, often proportionately - but not absolutely - quite large.

A study of corporate data analysts found two challenges to exploratory time series analysis: discovering the shape of interesting patterns, and finding an explanation for these patterns.<ref>{{cite book |doi=10.1109/vlhcc.2016.7739668 |chapter=Visual discovery and model-driven explanation of time series patterns |title=2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) |date=2016 |last1=Sarkar |first1=Advait |last2=Spott |first2=Martin |last3=Blackwell |first3=Alan F. |last4=Jamnik |first4=Mateja |pages=78–86 |isbn=978-1-5090-0252-8 |s2cid=9787931 |chapter-url=https://www.repository.cam.ac.uk/handle/1810/260285 }}</ref> Visual tools that represent time series data as [[Heat map|heat map matrices]] can help overcome these challenges.