Why Transformers Need Positional Encoding For Time Series: A Visual Guide

Transformers process all data in a sequence simultaneously, meaning the self-attention mechanism is inherently unaware of the order of observations. This is a critical issue for sequential data like time series, where order fundamentally changes meaning. To solve this, positional encoding is added to the input embeddings to provide the model with a sense of order and temporal structure. The article explains how sinusoidal positional encoding uses sine and cosine functions of different frequencies to create a unique vector for each position, allowing the model to understand relative distances between data points.