Time Series Basics
Trends, seasonality and moving averages.
Key idea
Trends, seasonality and moving averages.
In depth
A time series is data ordered in time. Typical parts: trend, seasonality and noise. Moving averages smooth noise, and autocorrelation measures dependence between a value and its past.
Worked example
Monthly sales show a rising trend with a December peak (seasonal).
Real-world uses
- Smoothing monthly sales to see trends
- Forecasting electricity demand with seasonal patterns
Common mistake
Treating consecutive observations as independent.
How to learn this
- Read the key idea and say it in your own words.
- Work through the worked example, then try a similar problem.
- Use the interactive lesson’s visual and mini-quiz to check the pattern.
Frequently asked questions
What is Time Series Basics?
Trends, seasonality and moving averages. It is a topic at the expert level in Probability & Statistics.
Where is Time Series Basics used in real life?
Real-world examples: Smoothing monthly sales to see trends; Forecasting electricity demand with seasonal patterns.
What should I learn before Time Series Basics?
Make sure you are comfortable with Poisson Process, the previous topic in Probability & Statistics.
What is a common mistake with Time Series Basics?
Treating consecutive observations as independent.
How can I practise Time Series Basics?
Open the interactive lesson, play with the visual, then answer the three-question mini-quiz. Getting two right marks the topic complete.
What comes after Time Series Basics?
Move on to Experimental Design next.
Related expert topics in Probability & Statistics
- Skewness and Kurtosis – Measures of asymmetry and tail heaviness.
- Residuals and R² – Error of a fit and proportion of variance explained.
- Simpson’s Paradox – A trend reverses when groups are combined.
- Law of Total Probability – Split an event over a partition.
- Bayes’ Theorem – Update beliefs with evidence.
- Medical Tests and Base Rates – Why positive results can still be probably false.