Skip to 0 minutes and 10 secondsHello I'm Galit Shmueli, a professor at National Tsing Hua University's Institute of Service Science I design and teach courses on Business Analytics to help students understand not just how to crunch data but why to crunch data. People, societies, products and processes generate lots of data. Data science and business analytics have evolved as more and more data becomes available at higher resolution and at higher frequency. Quantitative forecasting is the science of using time series data for generating forecasts. In other words, extrapolating a series of measurements into the future. Forecasting plays an essential role in decision making in almost any environment you can imagine where data are collected over time. Here are just a few examples.
Skip to 1 minute and 2 secondsForecasting the number of hourly or daily customers at a restaurant, bank, or other service provider can help with staffing decisions, inventory management, cash flow planning, service decisions, and even space design and choice of technologies to use. Forecasting is essential also in transportation. Think about bicycle sharing systems. Forecasting the demand for bicycles at each station at different times during the day can help with smarter distribution of bikes expansion of bike stations and planning new locations and bike routes. It can even help with maintenance scheduling. With today's Internet of Things (IoT) automated sensor data can help forecast usage of offices, restrooms, vending machines, and other facilities. Such forecasts can help with staffing, replenishment, and other service operations.
Skip to 1 minute and 59 secondsIt can also support environmentally minded decision making and design. In this course, we’ll focus on how to create a forecasting solution. You'll learn about different popular forecasting methods and algorithms but we're also going to focus on the entire forecasting process. This means we're gonna look at how to define a forecasting problem, how toevaluate the performance of a forecasting method, and, importantly, how to tie the forecasting analytics with the business problem. This course is for you if you'd like to extend your predictive analytics capabilities to the forecasting domain and time-series domain or if you have to generate forecast for your job. Maybe you'd like to become more knowledgeable consumer of forecasts.
Skip to 2 minutes and 50 secondsOr maybe you're just curious to learn scientific and practical approach to forecasting time series. The learning in this course is based on weekly activities In each week, you'll be expected to read a chapter in the textbook, to watch a few short videos, and to work on an activity or analysis related to forecasting. Finally you'll engage in an online forum. This combination is designed to help you learn about forecasting in an active and engaging way. I hope you now have a better idea about what this course is about and what you might learn from it. Welcome to the community of learners on time series forecasting or as we say here 歡迎光臨（Huānyíng guānglín）！