# Forecasting in the light of Big Data

## TL;DR

This paper critically examines the impact of big data on forecasting accuracy, arguing that more data does not always lead to better predictions and emphasizing the importance of combining modeling with data analysis.

## Contribution

It provides a critical assessment of big data's role in forecasting, highlighting the limitations of data-only approaches and advocating for a balanced modeling and data analysis strategy.

## Key findings

- More data does not necessarily improve forecast accuracy.
- A hybrid approach of modeling and data analysis is recommended.
- Weather forecasting exemplifies the limits of data-driven predictions.

## Abstract

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann.