Demand Management: Forecasting Methods

Related lectures (35)

Covers the Obermeyer solution approach for production planning and the impact of early market information.

Covers count data models and Poisson regression, then transitions to univariate time series analysis for forecasting economic variables.

Delves into the Sport Obermeyer case study, exploring supply chain challenges, forecasting methods, and production planning.

Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.

Explores methods for predicting electricity demand evolution and estimating consumption.

Explores binary choice models like probit and logit, as well as univariate time series analysis with ARIMA models for forecasting economic variables.

Explores ARMA models in time series analysis, covering model selection, forecasting, and precision assessment.

Explores error estimation in numerical integration and its applications in forecasting, emphasizing the Romberg method and Richardson extrapolation.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

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