Video Analytics — Real-Time Object Tracking & Activity

I will build a transformer-based model for your time series forecasting task — implementing PatchTST, Autoformer, or Informer depending on your sequence length and multivariate requirements, and benchmarking against LSTM and gradient boosting baselines to confirm the transformer approach delivers sufficient improvement to justify its computational overhead. Transformer models for time series are not universally better than simpler alternatives — recent research has demonstrated that linear models outperform transformers on many standard time series benchmarks, making a rigorous baseline comparison essential before committing to the more complex transformer architecture.

The model covers sequence preprocessing and patching, temporal positional encoding, attention mechanism configuration, training with your specified forecast horizon, evaluation against LSTM and linear baselines, and an inference pipeline for production forecasting.