How does Prophet handle seasonality and holidays better than other models?

Prophet, desarrollado por Facebook, maneja la estacionalidad y las vacaciones de manera más efectiva que otros modelos debido a su capacidad para modelar tendencias no lineales y capturar múltiples fuentes de variación en los datos. Este modelo es especialmente útil para datos con fuertes efectos estacionales y vacacionales, ya que puede identificar y modelar estos patrones de manera más precisa. Además, Prophet es capaz de manejar conjuntos de datos con observaciones nulas, faltantes u outliers, lo que lo hace robusto y efectivo para una amplia gama de aplicaciones, incluyendo la predicción de la demanda turística.

Sure, the robustness of Prophet lies in its ability to decompose the data into three components: trend, seasonality, and holiday effect 📊. The trend captures long-term progression of the data, while the seasonality addresses periodic fluctuations and the holiday component takes into account extraordinary events 📆. By modelling each one separately and combining them, Prophet optimizes the forecasting for your data 🎯.

Prophet models seasonality using Fourier series to provide flexible and smooth seasonal effects. It automatically detects and accommodates multiple seasonal patterns, handling daily, weekly, and yearly cycles. Users can also add custom seasonalities for non-standard patterns. This approach allows Prophet to adapt to both fixed and changing seasonal effects over time, a feature not always present in traditional time series models like ARIMA. The flexibility in handling various seasonal scales and the ease of incorporating complex seasonal behaviors make Prophet particularly effective for datasets with pronounced and intricate seasonal trends.

Prophet detects changepoints to identify significant shifts in the time series trend. It automatically identifies potential changepoints by analyzing historical data for abrupt trend changes. Users can control the sensitivity of changepoint detection through the changepoint_prior_scale parameter: higher values allow more flexibility and detection of more changepoints, while lower values enforce a smoother trend with fewer shifts. Prophet uses piecewise linear or logistic growth models, fitting separate trend segments around each detected changepoint. Users can also manually specify changepoints based on domain knowledge. This approach enhances forecast accuracy by adjusting for trend shifts.

There are several features that allow modeling seasonalities much easier than before: - Each seasonality component may be additive or multiplicative regarding the trend. - Conditional seasonalities allow us to model seasonalities that depend on other factors: summer, winter, weekend, etc. - In full Bayesian sampling mode (MCMC) Prophet also outputs uncertainty in each seasonality component.

You can think of a Fourier series as a musical composition where each term in the series represents a different musical note. Just as a complex piece of music can be broken down into individual notes played by various instruments, a complex time series with seasonality can be broken down into a combination of simpler sine and cosine waves. Prophet models seasonality with the help of this method.

Prophet models holiday effects by letting users input a list of holidays, which it integrates as additional regressors in the forecast. This allows for the accommodation of specific impacts on time series data tied to these dates. The model’s flexibility lies in its ability to adjust the influence of each holiday, even varying impacts in different years. Unlike traditional models like ARIMA, Prophet directly incorporates these irregular events, making it ideal for datasets where holidays significantly alter trends. This user-friendly approach enhances forecasting accuracy in scenarios where special events play a crucial role.

Usually holidays affect a few days before or after a specific date. In addition to usual holiday modeling, Prophet lets users specify lower and upper "windows" in order to extend the holiday. As mentioned by Vijay Barai, holidays are simply extra regressors for Prophet models. Specifying correct holidays not only helps to model them in the future, but also lets forecasting models to better learn trends, seasonalities and other components.

In theory, Prophet should outperform more traditional methods such as ARIMA, ETS and TBATS. In practice, it does require a greater level of expertise to tune and to keep the model tuned over time. Most companies will not have the in-house expertise to do so, and as a result Prophet output quality often lags other methods. This problem of in-house expertise is shared with other AI/ML methods such as LightGBM and XGBoost. Compared to those, Prophet tends to be more computing intense and less reliable, but slightly easier to set up initially.

In my experience, Prophet models are highly-explainable, which is very important when working with customers. Indeed, black-box solutions may have better accuracy on data science competitions. However, at the end of the day you have to defend your forecasts using strong arguments, concise dashboards and clear conclusions. Prophet helps to prepare by giving you all the numbers: trend, additive and multiplicative components along with other coefficients, and by plotting trend changepoints, uncertainty and other components.

SHAP library/ shaply values can challenge Prophet regarding information diagnostics as it provides “explainable AI” solution for the black box models. In my experience, we all applied Prophet for seasonality effect as it is used to be more intuitive comparing to black box models. Yet, black box models with SHAP gives us better accuracy and intuitive as well as information diagnostics, like coefs.

Prophet está diseñado para manejar de manera efectiva la estacionalidad y los días festivos. Utiliza un enfoque aditivo por defecto para modelar la estacionalidad, lo que significa que la estacionalidad se suma a la tendencia para obtener la predicción. Además, Prophet puede manejar automáticamente los efectos de los días festivos en las series de tiempo, lo que lo hace especialmente útil para conjuntos de datos que contienen vacaciones y otros días festivos.