ML Time Series Forecasting — ARIMA, Prophet or LSTM Model Build

I will build a time series forecasting model for your dataset — using ARIMA/SARIMA for classical statistical forecasting, Facebook Prophet for trend and seasonality decomposition, or an LSTM neural network for complex non-linear patterns — with model selection, hyperparameter tuning, forecast evaluation, confidence interval generation, and a production-ready forecasting pipeline. Time series forecasting models fail most commonly not from algorithmic choice but from incorrect stationarity treatment, leakage from using future data in training features, and evaluation using inappropriate metrics that hide the model's inability to forecast turning points.

The forecasting pipeline covers stationarity testing (ADF test), seasonal decomposition, model fitting with cross-validation using time-series-aware splits, forecast horizon configuration, RMSE, MAE, and MAPE evaluation on held-out test period, confidence interval plotting, and an inference function accepting new historical data and returning a forecast with uncertainty bounds. A Jupyter notebook with full documentation is delivered.

This service suits operations, finance, retail, and logistics teams needing accurate demand, revenue, inventory, or resource forecasts to support planning and decision-making.