Time Series Forecasting for Retail Sales
Build an R script that models historical retail sales as a time series and forecasts future values using ARIMA and exponential smoothing, comparing accuracy between methods. Learners practice time series decomposition, model fitting with the forecast package, and accuracy evaluation.
- Estimate
- ~8.5h
- Steps
- 5
- Completed by
- 0
- Proposed by
- codeseed.app
forecast · tseries · ggplot2
Project roadmap
- 01
Prepare the time series data
~1.5hLoad historical sales data and convert it into a ts object with the correct frequency, handling missing periods.
- 02
Decompose and explore seasonality
~1.5hDecompose the series into trend, seasonal, and residual components and visualize each.
- 03
Fit ARIMA and ETS models
~2hFit an auto.arima model and an exponential smoothing model using the forecast package.
- 04
Compare forecast accuracy
~2hSplit into train/test sets, generate forecasts, and compare accuracy metrics like RMSE and MAPE between models.
- 05
Visualize forecasts with confidence intervals
~1.5hPlot the forecasts with prediction intervals against actual held-out values.
Resources
- DocsR Manuals
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Tech stack