Understanding WFM Forecast Algorithm - Frequently asked questions
Frequently asked questions
What is the 8-weeks average forecasting algorithm in Zendesk WFM?
The 8-weeks average forecasting algorithm is a simple and reliable method used in Zendesk WFM. It calculates forecasts by averaging data from the past eight weeks. This algorithm is ideal for short-term forecasts up to 12 weeks, especially when…
How does the 8-weeks average with momentum algorithm work?
The 8-weeks average with momentum algorithm builds on the basic 8-weeks average by incorporating trends. This algorithm adjusts forecasts based on observed upward or downward trends over the past eight weeks. It averages data from the same time…
When should I use the Prophet algorithm in Zendesk WFM?
The Prophet algorithm is best used when you have strong seasonal patterns and a good amount of historical data. Developed by Facebook, Prophet is robust to missing data and outliers, making it suitable for time series with seasonal effects. It's…
What are the benefits of using XGBoost in Zendesk WFM?
XGBoost is a powerful machine learning algorithm for forecasting, known for its speed and accuracy. It is particularly effective when dealing with data that has outliers or when historical data is sparse. XGBoost uses an ensemble of decision trees…
How does LightGBM differ from XGBoost in Zendesk WFM?
LightGBM and XGBoost are both gradient boosting algorithms, but they differ in their tree growth methods. LightGBM uses leaf-wise growth, which can be faster and more accurate in certain scenarios compared to XGBoost's level-wise growth. It's…
What is NeuralProphet and when should it be used in Zendesk WFM?
NeuralProphet is an advanced forecasting model that combines traditional time-series methods with deep learning. It builds on Facebook's Prophet and is faster, offering more functionality. Use NeuralProphet when you have strong seasonalities and…
How does the LSTM Neural Network work in Zendesk WFM?
The LSTM Neural Network is a sophisticated model for capturing long-term and short-term patterns in time series data. LSTM is ideal for medium to high volume workstreams with strong seasonality and repeated events. It uses a series of gates to…
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