Build a time-series forecasting model

You are given a univariate or multivariate time-series dataset with timestamps and numeric targets. Predict the next H steps. Describe your preprocessing: resampling and alignment, timezone handling, missing-data imputation, outlier treatment, detrending/seasonality decomposition, and scaling. Propose at least two modeling approaches—one classical (e.g., ARIMA/ETS) and one deep learning (e.g., LSTM/TCN/Transformer)—and justify your choice of loss (MAE/RMSE/Huber/Quantile), multi-step strategy (recursive/direct/multi-output), and how to incorporate covariates such as holidays and known future features. Define a non-leaky evaluation protocol with walk-forward validation/backtesting and appropriate metrics (MAE, RMSE, sMAPE/MAPE, quantile loss for P50/P 90). Explain how you would detect concept drift and recalibrate or retrain. Provide concise pseudocode for training and inference.