Autoregressive (AR) Model Explained | Time Series Forecasting in Python (End-to-End) скачать в хорошем качестве

Autoregressive (AR) Model Explained | Time Series Forecasting in Python (End-to-End)

Autoregressive (AR) Model Explained | Time Series Forecasting in Python (End-to-End) In this video, we build a complete Autoregressive (AR) time series forecasting model in Python using Statsmodels. Starting from raw data, we go step by step through data preparation, visualization, stationarity testing, lag selection, model training, forecasting, evaluation, and residual diagnostics — exactly how it’s done in real projects. We use a daily coffee sales dataset to explain how an AR model predicts future values using past observations only, making this video perfect for beginners, students, and working professionals. 🔍 What You’ll Learn: What is an Autoregressive (AR) model and how it works When and why AR models are used in time series How to check stationarity (ADF & KPSS tests) Rolling mean & variance explained How to select the best AR lags using AIC ACF and PACF plots explained Proper train–test split for time series Forecasting future values with AutoReg Model evaluation using MAE, RMSE, and MAPE Residual analysis and Ljung-Box test How to validate if your AR model is statistically correct 🛠️ Tools & Libraries Used: Python Pandas Matplotlib Statsmodels Scikit-learn Jupyter Notebook #timeseriesanalysis #timeseries #ar