Predict future trends with ARIMA, Prophet & LSTM models
Load historical data and understand time series components — trend, seasonality, and noise.
Master autocorrelation analysis, differencing for stationarity, and rolling statistics to prepare data for forecasting models.
Build and tune ARIMA models for time series forecasting with proper parameter selection and diagnostics.
Use exponential smoothing and a simple regression baseline for trend/seasonality — pure-Python alternatives to Prophet and LSTM that run in the browser.
Compare ARIMA, Prophet, and LSTM using proper time series evaluation metrics and backtesting.