Time Series Analytics with RapidMiner
Who Should Attend?
DATA ANALYST and DATA SCIENTIST involved in Time Series Data.
Time Series Analysis with RapidMiner is a course regarding the analysis and handling of time series data science techniques. It introduces basic concepts in time series analysis such as lagging, windowing, and exploratory data analysis techniques such as moving averages, integration, and differentiation. It also introduces the most common analytical methods for modeling specifically with time series data such as ARIMA and seasonality forecasting.
This course requires either the successful completion of the basic-level training courses (Data Mining and Predictive Analytics with RapidMiner: Foundations & Advanced) or RapidMiner Analyst Certification exam (or functional equivalence in terms of knowledge of RapidMiner and basic data science).
- Understand of how RapidMiner Studio supports the analysis of time series data
- Execute techniques for basic analysis of series data
- Understand the most important differences between time series data techniques
- Choose the most suitable techniques based on the project objectives
- Utilize several different approaches to predictive modeling using time series data
1. Introduction to Time Series Data
2. Exploratory Data Analysis for Series Data
- Moving Averages
- Fourier Transformation and Logarithms
- Advanced Series Transformations
3. Series Aggregation and Summarization with Windowing
4. Predictive Modeling
- Using ARIMA Methods
- Using Exponential Smoothing Forecasting Methods
- Holt Winters for Seasonality Adjustments
5. Windowing and Predictive Modeling
- Using Conventional Machine Learning Algorithms
6. Other Time Series Data Considerations