The importance of creating stable time-series forecasts.

Are you ready to lead in the Age of AI? Make sure you understand how to construct time series forecasts.

Which chart would you pick from the below charts to create a more stable sales forecast? AI Leaders understand the need to convert non-stationery data into stationery. This results in more consistent statistical properties (i.e., the mean, variance, and autocorrelation do not change over time), which helps neural networks learn the important patterns easier. The first chart may look interesting and easier to interpret. However, the second chart is the "money" chart. Feed that into your neural networks and the resulting forecasts will be more accurate. 

AI Leaders should understand which charts are important and what questions to ask. This will make the LLM prompts questions and conversations with the engineering team better.

If you are not sure if a dataset is stationery, ask your engineer (or language model) to complete the ADF test (Augmented Dickey Fuller Test). If the p value is below 0.05 or the test statistic is lower than the critical value, your dataset is stationery and you've created the money chart for your model. What are the best neural networks for stationery data? Give RNN and its specialized version LSTM a try. These designs utilize sequential data effectively and are able to maintain memory stores to understand which information to remember and which to forget when forecasting data. This design optimizes computational costs and processing speed.

To be a successful AI leader, you must know which charts you are looking for and what questions to ask. This will make your LLM prompts and discussions with the engineering team more impactful. Remember, the goal is to utilize data to drive decision-making.

Which forecast would you pick?