From the course: Time Series Analysis and Forecasting with GPT-4o
Join today to access over 24,800 courses taught by industry experts.
Predicting with exponential smoothing model - GPT Tutorial
From the course: Time Series Analysis and Forecasting with GPT-4o
Predicting with exponential smoothing model
Aside from ARIMA exponential smoothing is our go to horse for predicting trends and patterns over time. But how does this horse differ from the ARIMA horse? Well, ARIMA is ideal for data sets where the data points relate closely to their previous values. It's great for situations where yesterday's data has a strong influence on today's, for example, stock prices today depend heavily on yesterday's prices. Exponential smoothing (ETS) is perfect for data that shows clear trends and seasonal patterns. It's great for predicting things like retail sales, which go up during the holidays and then drop afterward. ARIMA assumes the time series is stationary, while ETS can be applied directly to non-stationary data. ETS is a fantastic tool because it breaks down time series data into three components: other, trend, and seasonal. Each of these plays a crucial role in shaping our predictions. First, let's talk about the trend component. This captures whether the data shows a long term increase…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.