Time Series Forecasting
This chapter provides a high-level overview of time series forecasting and related analysis. It starts by pointing out the clear distinction between standard supervised predictive models and time series forecasting models. It provides a basic introduction to the different time series methods, ranging from data-driven moving averages to exponential smoothing, and also discusses model-driven forecasts including polynomial regression and lag-series-based ARIMA methods. Finally it explains how to implement lag-series-based forecasts using the Windowing operation using RapidMiner. It points out that the implementation of time series in RapidMiner is based on a hybrid concept of transforming series data into ...
Get Predictive Analytics and Data Mining now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.