Time Series Analysis
Learn to analyze and forecast data collected over time, using autocorrelation and trend detection.
Time Series Analysis is a free R Academy lesson on CoddyKit. This is lesson 2 of 3. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 3 lessons in total.
Introduction to Time Series Analysis
Time series analysis is used to study data points collected over time, identifying trends, seasonality, and patterns.
Loading Time Series Data
Use the ts() function to create a time series object in R.
data <- ts(c(100, 120, 130, 150, 170, 180), start=c(2020,1), frequency=12)
plot(data, main='Time Series Data')Decomposing Time Series
The decompose() function splits time series into trend, seasonal, and random components.
decomposed <- decompose(data)
plot(decomposed)Identifying Trends
Moving averages smooth fluctuations to reveal long-term trends.
library(zoo)
trend <- rollmean(data, k=3, fill=NA)
plot(data, type='l')
lines(trend, col='red', lwd=2)Seasonality Detection
Seasonality refers to repeating patterns at fixed intervals.
plot(decomposed$seasonal, main='Seasonal Component')Autocorrelation in Time Series
The acf() function measures correlations between past and present values.
acf(data, main='Autocorrelation Plot')Forecasting with ARIMA
ARIMA (AutoRegressive Integrated Moving Average) is a popular time series forecasting model.
library(forecast)
model <- auto.arima(data)
forecasted <- forecast(model, h=5)
plot(forecasted)Evaluating Forecast Accuracy
Use Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to measure forecast accuracy.
accuracy(forecasted)Summary
In this lesson, you learned:
- How to create and analyze time series data.
- How to detect trends, seasonality, and autocorrelation.
- How to forecast using ARIMA and evaluate predictions.
Learn R with an AI tutor — free
Write and run real code in your browser, get instant help from a 24/7 AI tutor, and pick up where you left off on the web or in the app.
- Courses
- 43
- Lessons
- 159
Frequently Asked Questions
Is the “Time Series Analysis” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “Time Series Analysis”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 3 lessons in total.
What will I learn in “Time Series Analysis”?
Learn to analyze and forecast data collected over time, using autocorrelation and trend detection. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 2 of 3.
How long does the “Time Series Analysis” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this R Academy lesson?
Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Regression Models
- Time Series Analysis
- Machine Learning Fundamentals