For a company observing seasonal sales patterns, which time series model would be best suited to forecast future sales?
- Autoregressive Integrated Moving Average (ARIMA)
- Exponential Smoothing State Space Models (ETS)
- Long Short-Term Memory (LSTM) Networks
- Seasonal Decomposition of Time Series (STL)
Seasonal Decomposition of Time Series (STL) is well-suited for capturing and forecasting seasonal patterns in time series data. It decomposes the time series into components such as trend, seasonality, and remainder. Other models like ARIMA, ETS, and LSTMs may be used for different patterns but STL is specifically designed for seasonality.
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