Time Series Lesson 46 – Transformers | Dataplexa

Transformers for Time Series Forecasting

Until now, we relied on sequential models like RNNs, LSTMs, GRUs, and Attention-based variants. Transformers take a completely different approach.

Instead of processing time step by step, transformers look at the entire sequence at once.

The Real Problem Transformers Solve

Imagine forecasting electricity demand for a large city.

Sequential models struggle to connect distant points. Transformers are built to handle this naturally.

Transformers rely on self-attention. Each time step can directly look at every other time step.

No recurrence. No memory bottlenecks.

Below is a simulated electricity demand series with:

Transformers remove this limitation.

In transformers, every timestep computes attention scores with every other timestep.

What you’re seeing:

Transformers do not know time order by default.

So we inject position information explicitly.

Python: Transformer Attention Logic

# Q, K, V = Query, Key, Value matrices

scores = Q @ K.T / sqrt(d_k)
weights = softmax(scores)

output = weights @ V

Important insight:

This is why modern forecasting systems increasingly rely on transformers.

Below shows how transformers smooth noise while preserving structure.

They shine when data volume and complexity are high.

Q1. Why don’t transformers suffer from long-term memory loss?

Because every timestep directly attends to all others without passing information sequentially.

Q2. Why is positional encoding necessary?

Because transformers do not inherently understand time order.

Next lesson: N-BEATS — a transformer-inspired architecture built specifically for forecasting.