Can we use past data to predict the future?

13 Tem 12:34

One method that has always stood out to me in data analytics and future forecasting is *time series analysis*. I’ve worked on several automation projects—like logistics and sales forecasting—using ARIMA or LSTM models, where the goal is to analyze trends and seasonal patterns in historical data to predict future demand. The most critical part of this approach is *data cleaning* and *proper feature engineering*—if unnecessary noise isn’t removed and seasonal effects aren’t accounted for, the predictions can deviate significantly. When it comes to reliability, I try to use *cross-validation* and *stress-test scenarios* as much as possible. For example, unexpected crises like COVID-19 can completely disrupt model predictions—real-world situations often hinge on *external shocks*. Adding scenario-based approaches (optimistic, pessimistic, most likely) to forecasting models can be a lifesaver in these cases.