Introduction
Forecasting helps organisations look ahead and act early. AI improves forecasting by finding patterns that simple methods miss. But the goal is not perfect prediction; it is better decisions: staffing the right number of nurses next week, ordering stock before a spike, or planning bed capacity ahead of a flu surge. This blog explains what AI adds, when simpler methods are enough, and how to use forecasts safely (Makridakis, Spiliotis and Assimakopoulos, 2018; Hyndman and Athanasopoulos, 2021).
What AI brings
Classical methods like exponential smoothing and ARIMA work well for stable, seasonal series. AI methods—gradient boosting, random forests, LSTMs, and transformers—handle complex patterns, many related signals, and interactions such as “demand increases when the weather is dry and there is a holiday next Monday”. They can learn non‑linear effects and combine hundreds of inputs, from search trends to staffing levels. The trade‑off is complexity and the need for more data (Makridakis, Spiliotis and Assimakopoulos, 2018).
Data and features
Good forecasts start with reliable data. Clean missing values, align calendars, and remove obvious outliers only when you understand the cause. Feature engineering helps: add holiday flags, moving averages, and lagged versions of important variables. For hospitals, include clinic schedules and public holidays; for retail, include promotions and price changes; for transport, include weather, events, and roadworks (Hyndman and Athanasopoulos, 2021).
Hierarchies and aggregation
Businesses forecast at multiple levels—total, region, and product. Reconciliation methods keep numbers consistent across levels. For example, forecasts for regions should sum to the national total. This reduces confusion and supports planning. Many modern libraries include automatic reconciliation options (Hyndman and Athanasopoulos, 2021).
Uncertainty and decision‑making
Every forecast is uncertain. Instead of one number, show ranges (for example, a 50% and a 95% interval). Use these ranges to trigger actions: if the 95% upper bound for admissions is above capacity, prepare surge plans. If the lower bound for sales is below target, plan discounts. This turns uncertainty into practical steps (Makridakis, Spiliotis and Assimakopoulos, 2018).
Explainability
People trust forecasts they can understand. Show the main drivers with tools like SHAP values or simple what‑if sliders. Compare the model with a naïve benchmark (e.g., “same as last week”). Keep a weekly report of accuracy, bias, and recent changes. If accuracy drops, check for data issues, model drift, or structural changes such as a new clinic or product line (Hyndman and Athanasopoulos, 2021).
When simple wins
Do not over‑engineer. For many series with short history, a simple seasonal naïve method or exponential smoothing will beat complex AI. Use AI where you have enough data and a genuine need to capture many signals. Always compare against simple baselines (Makridakis, Spiliotis and Assimakopoulos, 2018).
Ethics and impact
Forecasts can affect staffing and patient care. Build with fairness in mind, avoid using sensitive attributes, and audit for unintended bias. Communicate limits clearly. Use forecasts to support—not replace—professional judgement. This leads to safer and more accepted decisions.
Conclusion
AI makes forecasting more powerful, but success depends on clean data, clear ranges, and honest comparisons with simple methods. Design for decisions, explain results, and monitor performance. This approach delivers real value without hype (Makridakis, Spiliotis and Assimakopoulos, 2018; Hyndman and Athanasopoulos, 2021).
References (Harvard style)
Makridakis, S., Spiliotis, E. and Assimakopoulos, V. (2018) ‘Statistical and Machine Learning forecasting methods: Concerns and ways forward’, PLoS ONE, 13(3), e0194889.
Hyndman, R.J. and Athanasopoulos, G. (2021) Forecasting: Principles and Practice. 3rd edn. Melbourne: OTexts.
Lim, B. and Zohren, S. (2021) ‘Time Series Forecasting With Deep Learning: A Survey’, Philosophical Transactions of the Royal Society A, 379(2194), 20200209.