These variables could include holiday markers, marketing spending, weather data, or any other external data that correlates with the time series you are forecasting.

PatchTST specializes in segmenting time-series data into “patches,” allowing precise forecasting for irregular or noisy datasets, particularly in healthcare and environmental monitoring.

Time series graphs are intuitive, helping you relate a metric to time. Marketing analysts are often faced with choosing a data visualization that speaks to managers and colleagues interested in ...

Study suggests that judgmental forecasting by non-experts of trends in time-series data, such as weekly sales data, is consistently lower when the information is displayed in bar chart format as ...

When it comes to using AI in the staffing sector, companies have to approach it with pragmatism.