Advanced Sales, Collection and Purchasing Forecasting

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Sales, revenue and purchase forecasting in PRIOR uses a model selection algorithm. To provide accurate and timely predictions, we use a combination of machine learning technologies and advanced statistical techniques. In this way, for each prediction generated in the platform, a thorough evaluation of different prediction techniques is run and the most appropriate one is used.

Models Implemented

Ridge Regression

This is a linear regression technique that uses a penalty that prevents overfitting, meaning the model does not overfit the training data and therefore generalizes in a better way to new data.

XGBoost (Extreme Gradient Boosting)

XGBoost is a decision tree-based machine learning algorithm that uses the “boosting” technique. This method builds models from training data in an iterative manner, correcting errors from previous models.

LSTM (Long Short-Term Memory)

This is a special variant of recurrent neural networks (RNN), specifically designed to avoid long-term problems. They are ideal for time series and sequences due to their ability to remember previous inputs.

MLP (Multilayer Perceptron)

It is a form of artificial neural network. MLP networks can capture non-linear relationships in data and are especially useful when patterns in the data are complex and difficult to discern with simpler methods.

Model Selection Process

Not all datasets are created equal. Therefore, we implement a rigorous selection process to determine which model best fits the data in question. Through cross-validation techniques and performance metrics such as mean error, we determine which of the models provides the most accurate predictions.

Training and Optimization

Once the model is selected, we proceed to train it using our historical data. During this process, the model “learns” the underlying relationships and patterns in the data. In addition, we perform a series of optimizations to ensure that the model is properly tuned.

Forecast and Deployment

With the trained and optimized model, we make forecasts for the following year. These predictions are then integrated into our platform, providing users with valuable information on future sales, revenue and purchases.

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