Lectures related to Forecasting & Long Memory: Time Series

Explores point estimation methods like MOM and MLE, discussing bias, variance, and examples.

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Optimality in Statistical Inference

Delves into the duality between confidence intervals and hypothesis tests, emphasizing the importance of precision and accuracy in estimation.

Demand Forecasting: Model Initiation

Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.

How Memory Functions

Delves into sensory, short-term, and long-term memory processes, including iconic and echoic memories, rehearsal techniques, and the working memory model.

Time Series Analysis: ARIMA and Seasonal Models

Covers ARIMA, ARI, and seasonal models for time series analysis.

Smart, Connected Products

Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.

Functional Factor Models: Forecasting Mortality Curves in Japan

Explores high-dimensional functional factor models for forecasting mortality curves in Japan, discussing estimation, consistency, and application.

Stochastic Processes: Basics & Stationarity

Covers signal generation, statistical relations, stationarity, white noise, and orthogonality in stochastic processes.

Demand Forecasting: Methods and Steps

Discusses demand management, forecasting methods, and steps in demand forecasting, emphasizing the importance of accurate forecasts.

Error Estimation in LHS

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Quantitative Risk Management: Volatility Modeling

Covers volatility modeling in quantitative risk management, including ARMA, ARCH, GARCH models, and forecasting.

Networked Control Systems: Stability Analysis and Examples

Explores the impact of delays and sampling on networked control systems' stability, with examples and simulations.

Forecasting Methods: Expert Approaches and Professional Predictions

Explores forecasting methods, professional predictions for the Swiss economy, and the quality of anticipations in real estate and economic sectors.

Deep Learning: Recurrent Neural Networks

Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.

Estimation and Forecasting in Time Series

Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.

Why Revise Courses During the Semester MOOC: Learning to study science and engineering

Highlights the significance of revising course material during the semester to improve learning outcomes.

Forecasting Methods: Expert Approaches and Professional Predictions

Explores forecasting methods, professional predictions, biases, and challenges in economic trend forecasting.

SmartGrid: Monitoring and Control

Explores the deployment of PMUs for monitoring the Active Distribution Network.

Demand Management Optimization

Covers demand management optimization, focusing on forecasting methods and model initiation.

Forecasting & Long Memory: Time Series

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