Transformer neural networks have outpaced previous architectures and become the foundation for cutting-edge language models.

Below are the key differences between transformers and other neural networks:

1. Self-attention
The self-attention mechanism simultaneously evaluates relationships between all elements in a sequence (such as words in a text). This enables the model to grasp the global context, even when elements are far apart.
In contrast, RNN/LSTM architectures process data sequentially, making it harder to capture long-range dependencies due to the vanishing gradient problem. CNN models, on the other hand, focus on local patterns (such as n-grams), but often overlook broader contextual relationships.

2. Parallel processing
Transformers leverage matrix operations to process entire sequences in parallel, significantly speeding up training compared to RNNs and LSTMs that process data sequentially. For example, when working with a sentence of 10 words, a transformer can process all words at once, while an RNN would handle them one by one over 10 steps.

3. Non-recurrent architecture
Transformers are built on an encoder-decoder structure, where both components consist of layers of self-attention and feed-forward neural networks.
Unlike traditional models, transformers do not rely on recurrent (RNN) or convolutional (CNN) blocks as their core building elements.

4. Positional encoding
Since transformers process all elements in parallel rather than sequentially, they need a way to capture the order of elements in a sequence. This is achieved through positional embeddings—special vectors (e.g., sinusoidal functions or trainable embeddings). In contrast, RNNs and LSTMs inherently account for order through step-by-step processing.

5. Scalability
Transformers handle large datasets efficiently and scale well on GPUs/TPUs due to their parallel processing capabilities. This scalability has paved the way for the development of massive models like GPT-4, BERT and T5.

Originally designed for machine translation tasks, transformers now dominate areas like text generation, classification, summarization, and even computer vision.

Source: ScienceDirect

The Temporal Fusion Transformer (TFT) is an advanced ML model designed for time series forecasting. By blending transformer architecture with powerful mechanisms for capturing temporal dependencies, it excels at processing diverse data types and enhancing the accuracy of predictions.

The TFT model is particularly effective in addressing industry-specific challenges, including:

• Demand forecasting in retail and FMCG
• Network load prediction in the energy sector
• Financial forecasting for stock prices, volatility, and more
• Inventory optimization in logistics

For example, the BaOne retail solution built on the TFT model can forecast sales and deliver interval predictions, factoring in historical trends, holidays (future indicators) and regional data (static features), while pinpointing the most impactful days and variables.

Key advantages of TFT Models

• High Accuracy: Achieved through the combination of LSTM and transformer architectures.
• Interpretability: Attention weight visualizations offer clear insights into the factors influencing the forecast.
• Flexibility with diverse data: Seamlessly handle static, dynamic and future features.
• Interval forecasting: Estimate uncertainty through quantiles.