Triple Exponential Smoothing, FB Prophet Leveraging historical weekly unit sales dta for past 2 years, we predicted unit sales for selected products for the next 5 weeks.
Exponential smoothing is a time series forecasting method for univariate data. Time series methods like the Box-Jenkins ARIMA family of methods develop a model where the prediction is a weighted linear sum of recent past observations or lags. Exponential smoothing forecasting methods are similar in that a prediction is a weighted sum of past observations, but the model explicitly uses an exponentially decreasing weight for past observations. Specifically, past observations are weighted with a geometrically decreasing ratio.
Source: https://machinelearningmastery.com/exponential-smoothing-for-time-series-forecasting-in-python/
https://facebook.github.io/prophet/
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
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