{"id":1420,"date":"2022-07-21T12:51:46","date_gmt":"2022-07-21T12:51:46","guid":{"rendered":"https:\/\/ignos.blog\/machine-learning-sales-prediction"},"modified":"2025-01-16T15:25:27","modified_gmt":"2025-01-16T15:25:27","slug":"machine-learning-sales-prediction","status":"publish","type":"post","link":"https:\/\/ignos.blog\/en\/machine-learning-sales-prediction","title":{"rendered":"Sales prediction using Machine Learning"},"content":{"rendered":"\n

One of the facilities offered byMachine Learning (ML<\/strong>) is the possibility of making time series predictions. To cite some applications, predictions of economic, weather or climate, health, or business indicators. <\/p>\n\n

In the Business Intelligence (BI) environment, a type of prediction that would be of great value could be the prediction of sales. This prediction, for example, would allow us, among other things, to efficiently manage procurement, stock, logistics or rethink marketing actions. <\/p>\n\n

Prediction algorithms<\/h2>\n\n

As mentioned in the article Artificial Intelligence applied to BI<\/a>, there are currently multiple tools available to develop Machine Learning algorithms, many of which are accessible to small and medium-sized companies.<\/p>\n\n

For time series prediction we can currently rely on algorithms based on “classical” statistics, such as ARIMA, Exponential Smoothing, Linear Regression Model or Random Forest, and algorithms based on neural networks such as Classical Recurrent Networks, LSTM, N-Beats, or Transformer type models, for example.<\/p>\n\n

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