Analysis

Hi, Recently, I had to work on a simple time-series analysis. I performed poorly since I never worked with time-series before. I believe in a deterministic world, and in general, I prefer to find the causality of a specific data behavior prior to a simple way of empiristic modeling. However, I understand the need for time-series analysis as not enough data available, the underlying processes understood, the complexity bearable, or the time/need for a proper process understanding. The goal is to make a prediction based on the previously observed observations. In a traditional sense (Arima), you look at the trend, seasonality, and cycles - in the more modern way, you throw the data into a model architecture (deep learning). In this context, I should mention the famous paper Statistical Modeling: The Two Cultures, while I prefer to use algorithmic models and treat the data mechanism as unknown. I would add that the underlying data mechanism is deterministic, and we should use collected data to get improved models. Anyway, let’s use the many resources in the time-series field to get better in this field. ...