Master Time Series Forecasting with R: Analyze & Predict

EDUCBA follows the structure of a time series before moving into more specialized forecasting models. Regression and decomposition lead into smoothing, ARIMA and seasonal variants. Its roughly nine-hour workload is a focused option for R users working with observations over time.

What you will learn

• Separate trend and seasonal patterns in a time series.
• Compare smoothing and regression-based forecasting approaches.
• Inspect ARIMA diagnostics and forecast accuracy.

Who this suits

Learners with basic R and statistics, particularly those analyzing operational or business time series.

Before you enroll

Basic R and statistics are explicit requirements. Historical forecast performance does not establish future accuracy; preserve the ordering of time when evaluating models.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Tool subscriptions, model API usage and cloud services may cost extra.

Put it into practice

Forecast a public monthly series with a seasonal baseline and one fitted model. Hold back the latest observations and compare errors before interpreting the results.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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