Using exponential smoothing model to predict future values based on previously observed values
Use forecasting today to optimize for tomorrow! Time series forecasting is the use of a model to predict future values based on previously observed values.
It is one of the prime tools of any buisness analyst used to predict demand and inventory, budgeting, sales quotas, marketing campaigns and procurement. Accurate forecasts lead to better decisions. Current visual implements well known exponential smoothing method for the forecasting. The prediction is based on trend and seasonality modeling. You can control the algorithm parameters and the visual attributes to suit your needs.
- NEW: support for tooltips on hover and selection
- The underlying algorithm requires the input data to be equally spaced time series
- Seasonal factor can be found automatically or set by user
- The choice of additive or multiplicative effect of each component can be found automatically or set by user
R package dependencies(auto-installed): graphics, scales, forecast, zoo, ggplot2, htmlWidgets, XML, plotly
Supports R versions: R 3.3.1, R 3.3.0, MRO 3.3.1, MRO 3.3.0, MRO 3.2.2
This is an open source visual. Get the code from GitHub: https://github.com/microsoft/PowerBI-visuals-forcasting-exp
- Can access external services or resources