Time-Series Forecasting

Learn time-series forecasting, from spreadsheets to AI models, to make better business decisions by evaluating forecasts, quantifying uncertainty, and communicating insights.

Identify trends, seasonality, and noise in time series to forecast future values while accounting for uncertainty and the differing costs of over- and underforecasting.

Apply naive, moving average, and trend forecasts in spreadsheets, compare accuracy with holdout validation, and recognize the limits of simple forecasting methods.

Understand prediction intervals to communicate forecast uncertainty, explain why intervals widen over time, and make better planning decisions based on forecasting risk.

Generate automated forecasts with BI tools, interpret ETS (Error, Trend, Seasonality)-based predictions, choose the right data granularity, and recognize the limits of black-box forecasting.

Understand the differences between spreadsheet, BI, and classical forecasting models, including their strengths, limitations, and appropriate use cases.

Understand how time-series frequency, granularity, and time indexing affect forecasting accuracy, seasonality, and model reliability.

Prepare time-series data in pandas by creating a DatetimeIndex, resampling data, validating quality, and engineering features for forecasting models.

Evaluate stationarity using rolling statistics and the ADF test, then apply transformations to prepare time-series data for reliable forecasting models.

Transform time-series data by testing for stationarity and applying differencing or log transformations to prepare data for forecasting.

Understand ARIMA and SARIMAX model components, select model orders with ACF and PACF, and incorporate seasonal patterns and external variables.

Evaluate forecast reliability using residual diagnostics, prediction intervals, and error metrics to determine whether a model is trustworthy for business decisions.

Build ARIMA and SARIMAX models in statsmodels, compare models with AIC and BIC, and generate forecasts with prediction intervals.

Validate time-series models using residual diagnostics, AIC/BIC, and forecast error metrics to select reliable models for production forecasting.

Use Prophet to generate scalable forecasts, incorporate seasonality and external regressors, and compare its strengths and trade-offs with ARIMA and SARIMAX.

Understand when to use deep learning for time-series forecasting and compare its strengths, limitations, and trade-offs with classical forecasting models.

Train neural forecasting models with N-BEATS, generate prediction intervals using quantile regression, and compare neural forecasts with classical models.

Apply foundation models for zero-shot forecasting, recognize when they outperform specialized models, and evaluate their trade-offs in accuracy, transparency, and control.

Apply zero-shot forecasting with foundation models, evaluate forecasts through backtesting, and compare results using forecast error metrics.

Review the complete time-series forecasting workflow, from data preparation and model selection to uncertainty, diagnostics, and business decision-making.