Predictive Modelling
& Optimisation

Predictive modelling is concerned with finding a function that optimally maps input data to a given output with the goal of making accurate predictions — this principle underpins time series forecasting, where historical data is analysed to identify patterns and predict future outcomes.

 

At its core lies the concept of the data-generating process (DGP), which refers to the underlying mechanism that produces the observed data. By understanding the DGP, businesses can develop more accurate models to leverage trends, seasonal behaviours, and other time-related insights. From planning inventory to anticipating economic shifts, this technique provides a structured framework for addressing uncertainty in dynamic environments (Hyndman & Athanasopoulos, 2021).

 

Methodology

Our forecasting methodology integrates both machine learning models and statistical methods to extract meaningful insights from time series data. Statistical methods, such as ARIMA and exponential smoothing, are particularly effective for identifying trends, seasonality, and random fluctuations, allowing us to build robust mathematical models. Machine learning models, on the other hand, excel at capturing complex, non-linear relationships in the data. By combining these approaches, we develop flexible predictive models that accurately project temporal patterns into the future (Makridakis et al., 2020).

 

This foundation is further enhanced by advanced feature engineering, incorporating external factors such as macroeconomic indicators (e.g., GDP, interest rates, CPI), social media sentiment and themes, competitor actions, customer demographics, and proprietary datasets. These external influences refine the models by accounting for variables beyond the historical data, further improving forecast accuracy (Petropoulos et al., 2022).

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Ensemble Methods

Building on these principles, we leverage ensemble forecasting techniques to develop robust predictive models. Ensemble methods combine multiple forecasts to enhance accuracy and reliability by utilising the strengths of individual approaches (Oliveira, 2015).

 

This multi-faceted methodology enables machine learning to automatically identify complex relationships between time series behaviour and exogenous variables (e.g. macroeconomic indicators), maintaining robustness against market noise while ensuring precise estimation of both short-term fluctuations and long-term trends.

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Conformal Prediction

To ensure reliability, we employ conformal prediction, a method that provides dynamic confidence intervals, ensuring that forecast ranges align with specified confidence levels. For example, in demand forecasting, conformal prediction not only highlights the most likely outcomes but also offers a clear range of possibilities, empowering decision-makers with actionable insights (Vovk, Gammerman, & Shafer, 2022).

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Model Validation

Ensuring the robustness and generalisability of our models is central to our methodology. To achieve this, we rigorously evaluate performance against holdout datasets, which simulate real-world conditions by testing the models on unseen data. This validation step helps us prevent model overfitting and ensures the reliability of our forecasts when applied to practical scenarios (Hyndman & Athanasopoulos, 2021; Petropoulos et al., 2022).

 

By combining statistical rigor, machine learning innovation, ensemble forecasting, and conformal prediction, our models transform uncertainty into quantifiable probabilities, enabling you to Predict, Plan, and Act with confidence.

 

References

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Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.

 

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M5 competition: Results, findings, and conclusions. International Journal of Forecasting, 36(1), 54-74.

 

Oliveira, M. (2015). Ensembles for time series forecasting. JMLR: Workshop and Conference Proceedings, 39, 360–370.

 

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., & others. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705-871.

 

Vovk, V., Gammerman, A., & Shafer, G. (2022). Algorithmic learning in a random world (2nd ed.). Springer.

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