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Home > Forecast processing

Forecast processing

Data Collection and Preparation

Gather historical data

Clean and preprocess data

Remove outliers and missing values

Check for data consistency and accuracy

Exploratory Data Analysis

Calculate summary statistics

Visualize data using charts and graphs

Identify trends and patterns

Conduct time series analysis

Model Selection and Building

Determine the forecasting method (e.g., exponential smoothing, ARIMA, machine learning)

Split data into training and testing sets

Select appropriate model parameters

Fit the chosen model to the training data

Model Evaluation

Validate the model using the testing data

Calculate forecast accuracy measures (e.g., MAE, RMSE)

Compare the model's performance against benchmarks

Identify any areas for improvement or adjustments

Forecasting and Reporting

Forecast future values based on the selected model

Generate predictions for the desired time period

Create a forecast report summarizing key findings and insights

Communicate the forecasted results to stakeholders

Monitoring and Updating

Monitor the forecasted values against actual outcomes

Assess the accuracy and reliability of the forecast

Update the model and re-evaluate periodically

Make necessary adjustments based on new data or changes in the environment

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