LSTM time series

When forecasting, I like to use 1 – weighted MAPE as an error metric to determine the fit of the model . I take the absolute error of the actual – predicted values at a daily level the aggregate all the errors up and divide by the total sales. For the Store 1 model, it has a forecast accuracy of 94.79%.27 Tem 2021

To use Prophet for forecasting, first, a Prophet() object is defined and configured, then it is fit on the dataset by calling the fit() function and passing the data . The Prophet() object takes arguments to configure the type of model you want, such as the type of growth, the type of seasonality, and more.26 Ağu 2020

At its core, the Prophet procedure is an additive regression model with four main components: A piecewise linear or logistic growth curve trend . Prophet automatically detects changes in trends by selecting changepoints from the data. A yearly seasonal component modeled using Fourier series.

What is Facebook Prophet and how does it work? Facebook Prophet is an open-source algorithm for generating time-series models that uses a few old ideas with some new twists . It is particularly good at modeling time series that have multiple seasonalities and doesn’t face some of the above drawbacks of other algorithms.19 Şub 2021

The Prophet algorithm is an additive model , which means that it detects the following trend and seasonality from the data first, then combine them together to get the forecasted values.