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Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models
- Time series analysis can be made simpler by starting from basics and focusing on intuition behind concepts.
- Understanding time series involves identifying trends, seasonality, and noise to make informed predictions.
- Baseline models like Naive Forecast, Seasonal Naive Forecast, and Moving Average provide simple yet effective forecasting.
- Baseline models like Moving Average can provide around 80% accuracy for business planning, making them valuable.
- Decomposing time series into trend, seasonality, and residuals is crucial for selecting appropriate forecasting models.
- Additive model in time series decomposition assumes that trend, seasonality, and residuals combine linearly.
- The stability of seasonal patterns over time indicates that an additive model is suitable for decomposition.
- Multiplicative models are preferred when seasonal effects scale with trend, capturing proportional changes.
- Implementing a Seasonal Naive model based on decomposition shows the forecasting accuracy and limitations.
- Evaluation metrics like MAPE are used to assess forecasting model performance and set benchmarks for future improvements.
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