Time Series and Forecasting: ARIMA, Smoothing and Leakage Explained

Why does shuffling your data ruin a forecast? Learn how time series really work, step by step, with numbers you can check by hand. In this episode of Machine Learning from Zero to Advanced, we break a series into trend, seasonality and residual, build lag features and moving averages, and compute autocorrelation by hand. Then we cover stationarity and differencing, and unpack the AR, MA, ARMA and ARIMA equations one symbol at a time. We also walk through exponential smoothing, time-based validation, forecast errors like MAE and MAPE with their caveats, how classical and machine learning forecasting compare, and the future leakage traps that quietly wreck real projects. 0:00 Introduction 1:11 Trend, Seasonality and Residual 3:10 Lags and Moving Averages 4:21 Autocorrelation 6:24 Stationarity and Differencing 8:07 AR, MA and ARIMA 11:39 Exponential Smoothing 13:18 Time-Based Validation 14:24 Forecast Errors 16:09 Classical vs Machine Learning 17:24 Future Leakage Traps 18:52 Recap and Next Steps #TimeSeries #Forecasting #ARIMA #MachineLearning #DataScience