Simple Time Series Prediction Modeling Using Tinybird

Time series predictions are a valuable tool for companies aiming to anticipate future events and make informed decisions, with various methods ranging from sophisticated machine learning algorithms to simpler statistical approaches. An example of a straightforward method is using the NYC taxi dataset to predict taxi pick-ups, where data from 2017 is used for training and 2018 for validation, leveraging the Tinybird Datasource API. The prediction assumes that the number of pick-ups mirrors the previous year, adjusted for average annual growth, and involves techniques like using the addYears function for date matching and adjusting for the day of the week. The model performs well with deviations typically under 10%, and improvements could include accounting for outlier dates and analyzing model performance to rectify inaccuracies. Further analysis could enhance the model's utility by examining pick-up patterns per taxi zone or hour, and future discussions will explore these advanced analyses.