Encoder

As explained in Time Series Forecasting Using Foundation Models

An encoder is the sequence-processing component of a transformer that receives token representations and transforms them into contextual representations.

An encoder is the sequence-processing component of a transformer. It receives embedded, position-aware time-series tokens and transforms them into contextual representations for the decoder. section 1.2

Each encoder combines self-attention with a feed-forward layer, and transformers usually stack many such layers. Unmasked self-attention relates each token to the known historical sequence, while multiheaded attention lets different heads learn patterns such as trends or seasonality. section 1.2.2

In time-series forecasting, the encoder:

• Learns importance weights and dependencies among historical tokens. section 1.2.2
• Builds increasingly deep representations as embeddings pass through stacked encoding layers. section 1.2.2
• Passes its output to the decoder, which uses that representation to generate predictions. section 1.2.3

Pattern-focused attention

Different attention heads can specialize in different properties of a time series: one may identify trends while another identifies seasonal patterns. A visualization may also show stronger attention to nearby time steps than distant history. section 1.2.2

Figure 1.5 The encoder is made of many encoders that have the same architecture. An encoder is made of a self-attention mechanism and a feed-forward layer.

Use the encoder diagram to see the repeated self-attention and feed-forward structure across stacked encoding layers. section 1.2.2

How Time Series Forecasting Using Foundation Models covers it

How deep it goes

Explains stacked layers, self-attention, and multiheaded attention, but defers their mathematical details.

Examples

Illustrates nearby-token attention and separate attention heads for trends and seasonal patterns in time series.

What it assumes

Assumes familiarity with sequences and embeddings while introducing transformer components and positional order.

What you can do after

Evaluate/understand how encoder representations support autoregressive time-series predictions.

How it connects

Connects embeddings and positional encoding to self-attention, feed-forward layers, multiheaded attention, and the decoder.

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Related concepts

• transformer architecturepart of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• autoencoderpart of · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
• variational autoencoderpart of · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• CLIPpart of · CLIP is a multimodal model and pretraining method that aligns image and text representations in a shared embedding space.
• U-Netpart of · U-Net is an encoder-decoder neural-network architecture with downsampling and upsampling paths connected by skip connections for combining features at multiple spatial scales.

This explainer was generated by Manning's knowledge graph from the text of Time Series Forecasting Using Foundation Models. The section links point to the parts of the book it draws on.