I have the 3-month sale(branch wise) and I want to predict next month sale. There are huge differences between total sale in between months. The simple average of the month's to predict next month sale is not valuable. delta(change sale in the month) between months is important and take into account. Any help, tips, pointers, recommendations, or directions would be extremely helpful and I would be eternally grateful!


The Facebook Prophet API might be the place to start. I imagine that the data is daily.

This package is avaiable either for R or Python, and it's very simple to use. You can get predictions easily, and if you have some knowledge about Time Series analysis you can tune some parameters to best fit your solution.

From the link above:

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.

Here and Example with R.


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