How Data Scientists Use Transformers for Time Series Forecasting

In this episode, Lucas and Luna explore how transformer architectures are reshaping time series forecasting. Using a real case from a regional utility company, they examine how replacing an LSTM model with a transformer cut prediction error by 12%. They break down the key differences: attention mechanisms capturing long-range dependencies, positional encoding for sequence order, and the trade-offs in training and inference. The conversation also covers practical deployment challenges—latency, memory, and model tuning—and why tools like Hugging Face's Time Series Transformer are making this shift accessible. If you're a data scientist deciding between RNNs, CNNs, and transformers, this episode gives you the concrete comparison you need. #DataScience #TimeSeries #Transformers #MachineLearning #Forecasting #AttentionMechanism #LSTM #EnergyForecasting #DeepLearning #SequenceModeling #Podcast #FexingoBusiness #BusinessPodcast #Technology #ArtificialIntelligence #NeuralNetworks #ModelDeployment #DataEngineering Keep every episode free: buymeacoffee.com/fexingo