Machine Learning Methods for Demand Forecasting

Optimizing forecasting models with machine learning methods increases accuracy, but it's essential to ensure external data is forecast-grade.

Combining traditional statistical approaches with machine learning

When forecasting time series data, machine learning is an application of artificial intelligence (AI) that provides forecasting models the ability to learn about historical demand patterns and anomalies and improve future demand prediction accuracy. This minimizes the difference between the predicted and actual demand values, allowing businesses to have better foresight into what’s coming. Compared to traditional methods like regression, ARIMA, or other statistical approaches, applying machine learning techniques can accelerate data processing speed and improve prediction accuracy. 

Neural networks have grown in popularity as demand forecasters look to improve forecasting accuracy, and a few stand out methods include LSTM and MLP. 

Long short-term memory (LSTM)

Developing LSTM models for time series forecasting is a newer solution that has sparked the interest of many demand forecasting practitioners. Typically, recurrent neural networks have to use persistent previous information but LTSM is specifically designed to learn long term dependencies.

Multilayer perceptrons (MLP) 

Multilayer perceptrons can be used to model univariate time series forecasting problems. But this method can only be applied to time series forecasts after lag observations are flattened into feature vectors.

Achieving forecasting accuracy isn’t possible without reliable, intelligent data

Demand intelligence is forecast-grade data that improves ML forecasting

Regardless of the machine learning method or combination of approaches, conducting precise forecasting with machine learning techniques can’t be achieved without accurate data sources, optimized to forecast external causal factors. 

Demand intelligence is forecast-grade data that provides demand forecasters visibility into external demand anomalies. Scheduled events, severe weather, school holidays, and health warnings are external factors that drive incremental demand spikes as well as decremental demand dips, usually resulting in forecasting anomalies. Accessing this data and training your models can significantly improve prediction accuracy and help businesses make better decisions around dynamic pricing, workforce optimization, inventory levels, and more.

With a demand intelligence API, demand forecasters can model the impact of external demand factors using one single source of truth. By following the demand forecasting guide, you can learn how to add features into your model and fine-tune demand prediction precision. 

It’s simple for your machine learning-based systems to automatically receive and use demand intelligence without your team wasting labor on sourcing, collating and aggregating the data themselves. Our data processing pipeline aggregates and verifies millions of causal factors to ensure data quality.  

Discover events that are relevant to your ML-powered services by searching within a geographical radius, using IATA codes or geographical place names. You can query multiple categories and locations simultaneously and get JSON responses. Integrate our data with any platform thanks to our easy-to-use API. We also have Python and JavaScript SDKs available. We use the OAuth 2.0 standard for authentication and access tokens are required to grant application access to the tools available in the API. We make your developers’ lives easier too, because they don’t need to spend precious time capturing, cleaning and maintaining disparate data sources. Plus, our API is versioned for easier maintenance, reducing operational costs 

You can’t prepare for what you don’t see coming

Harness the power of demand intelligence

Knowing the impact of demand causal factors like events will transform your business. The American Society of Hematology has a $45M estimated economic impact — and that's only one event in one city.

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