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April 7, 2024 - last updated
Real-world Applications and Use Cases

Machine Learning for Forecasting: Use cases and building your own

Reah Miyara
Reah Miyara
8 min read Apr 19, 2023

What Is Machine Learning Forecasting? 

Machine learning (ML) for forecasting involves using algorithms to learn patterns in historical data and make predictions about future events or trends. Forecasting is essential in various industries, such as finance, retail, healthcare, energy, and transportation, for planning and decision-making purposes. ML-based forecasting methods have become increasingly popular due to their ability to model complex relationships and adapt to new data.

This is part of a series of articles about machine learning for business.

Business Use Сases for Machine Learning Forecasting

Machine learning forecasting has a wide range of applications in business, helping organizations make data-driven decisions, optimize processes, and improve overall performance. Here are four key use cases of ML forecasting for businesses:

Financial Forecasting

ML algorithms can be used to predict financial outcomes, such as revenues, expenses, and profitability. By analyzing historical financial data, these models can help businesses make informed decisions about investments, budgeting, and resource allocation. Additionally, ML-based financial forecasting can assist in identifying potential risks and opportunities, allowing companies to proactively manage their financial health.  

Learn more in our detailed guide to machine learning for finance 

Supply Chain Forecasting

In supply chain management, ML forecasting can optimize inventory levels, production planning, and distribution strategies. By analyzing past supply chain data, ML models can predict demand fluctuations, supplier lead times, and potential disruptions. This enables businesses to maintain optimal stock levels, reduce waste, and minimize costs associated with excess inventory or stockouts. 

Price Prediction

ML algorithms can analyze factors such as market trends, consumer behavior, and competitor pricing to predict optimal pricing strategies for products and services. Accurate price predictions help businesses maximize revenues, improve customer satisfaction, and maintain a competitive edge in the market.   

Demand and Sales Forecasting

Machine learning can be employed to forecast customer demand and sales performance. By analyzing historical sales data, customer demographics, and external factors such as economic indicators and seasonal trends, ML models can predict sales volume and revenue. This information allows businesses to optimize marketing strategies, allocate resources effectively, and plan for future growth.

ML Models and Algorithms for Forecasting 

Time Series Forecasting

Time series forecasting involves predicting future values based on past observations in a time-ordered sequence. Common ML models used for time series forecasting include ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), and Prophet (a procedure developed by Facebook). These models capture various aspects of time series data, such as trends, seasonality, and cyclical patterns, to make accurate predictions.

Regression Algorithms

Regression algorithms model the relationship between a dependent variable (the target) and one or more independent variables (features). In forecasting, regression models can be used to predict future values based on historical data. Examples of regression algorithms include linear regression, logistic regression, ridge regression, and LASSO (Least Absolute Shrinkage and Selection Operator).

Tree-Based Algorithms

These algorithms construct decision trees or ensembles of trees to make predictions. They can be used for both regression and classification tasks, including forecasting. Examples of tree-based algorithms include decision trees, random forest, and gradient boosting machines (GBM). These methods are effective at capturing complex interactions between features and handling non-linear relationships in the data.

Auto-Regressive Algorithms

Auto-regressive models predict future values based on a weighted sum of past observations. In other words, they use previous data points as input features to predict the next value in a time series. Some popular auto-regressive algorithms include AR (AutoRegressive) models, VAR (Vector AutoRegressive) models, and RNNs (Recurrent Neural Networks) such as LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units), which are particularly effective in handling long-term dependencies in time series data.

Exponential Smoothing

Exponential smoothing techniques are used to forecast time series data by applying a weighted average of past observations, with more recent data receiving higher weightage. The most popular methods include Simple Exponential Smoothing, Holt’s Linear Trend Method, and Holt-Winters Seasonal Method. These methods are particularly useful when the data exhibits trends or seasonality and require a simple and interpretable forecasting model.    

How to Apply Machine Learning Forecasting: Step by Step 

Applying machine learning forecasting involves several steps, from data preprocessing to model evaluation. Here’s a step-by-step guide to implementing machine learning forecasting:

  1. Understand the problem: Clearly define the forecasting problem and the target variable you want to predict. Understand the domain and identify relevant factors that may influence the forecast.
  2. Gather historical data: Collect historical data that includes the target variable and any relevant features that may impact the predictions. Ensure that the data is collected at a suitable time interval (e.g., hourly, daily, or monthly) for your specific problem.
  3. Data preprocessing: Clean and preprocess the data by handling missing values, outliers, and any inconsistencies. You may need to aggregate, resample, or interpolate the data to match the required time intervals.
  4. Feature engineering: Extract relevant features from the data that may help improve forecasting accuracy. This could include lagged variables (past values of the target variable), moving averages, and other derived features. Consider incorporating external data, such as economic indicators or weather data, if relevant to the problem.
  5. Split the data: Divide the data into a training set and a test set. The training set is used to train the machine learning model, while the test set is used to evaluate its performance. Time series data should typically be split in chronological order to maintain the temporal dependencies.
  6. Select a forecasting method: Choose a suitable machine learning technique based on the problem, data characteristics, and required prediction accuracy. Common methods include ARIMA, ETS, LSTM, and ensemble methods.
  7. Train the model: Train the selected model using the training data. Fine-tune the model’s hyperparameters using techniques like grid search or random search, and cross-validation to find the best combination for your specific problem.
  8. Evaluate the model: Assess the model’s performance on the test set using appropriate evaluation metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Mean Absolute Percentage Error (MAPE). Compare the performance of different models to select the best one for your problem.
  9. Perform model diagnostics: Analyze the residuals (errors) of the model to ensure that they are randomly distributed and have no remaining patterns. This helps ensure the model has adequately captured the underlying patterns in the data.
  10. Forecast and validate: Use the trained model to generate forecasts for future time periods. Continuously validate the model’s performance by comparing its predictions to actual data as it becomes available. Update and retrain the model as needed to maintain its accuracy.
  11. Communicate results: Present the forecasts and any associated uncertainties to stakeholders in a clear and understandable manner. Visualization tools, such as line charts and confidence intervals, can help communicate the results effectively.

Machine Learning for Business with Aporia

Model monitoring is essential for maintaining the performance and reliability of machine learning forecasting models. It helps detect data drift, model degradation, anomalies, and ensures compliance and fairness. Key metrics to track include forecast accuracy, model stability, feature importance, data quality, and bias and fairness. Regular monitoring enables early identification of issues and maintains the accuracy, reliability, and equity of ML systems.

Aporia’s ML observability platform is the ideal partner for Data Scientists and ML engineers to visualize, monitor, explain, and improve ML forecasting models in production in minutes. The platform supports any use case and fits naturally into your existing ML stack alongside your favorite ML tools and frameworks. We empower organizations with key features and tools to ensure high model performance: 

Production Visibility

  • Single pane of glass visibility into all production models. Custom dashboards that can be understood and accessed by all relevant stakeholders.
  • Track model performance and health in one place. 
  • A centralized hub for all your models in production.
  • Custom metrics and widgets to ensure you’re getting the insights that matter to you.

ML Monitoring

  • Start monitoring production models in ±7 minutes.
  • Instant alerts and advanced workflows trigger. 
  • Customize monitors to detect data drift, model degradation, performance, etc.
  • Track relevant custom metrics to ensure your model is drift-free and performance is driving value. 
  • Choose from our automated monitors or get hands-on with our code-based monitor options. 

Explainable AI

  • Get human readable insight into your model predictions. 
  • Simulate ‘What if?’ situations. Play with different features and find how they impact predictions.
  • Gain valuable insights to optimize model performance.
  • Communicate predictions to relevant stakeholders and customers.

Root Cause Investigation

  • Slice and dice model performance, data segments, data stats, or distribution.
  • Identify and debug issues.
  • Explore and understand connections in your data.

To get a hands-on feel for our ML observability platform, we recommend:

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