Introduction

Time Series Analysis in KNIME is a critical technique in data science used to analyze and predict trends over time. From financial forecasting to demand prediction, time series modeling helps businesses make data-driven decisions by identifying patterns and seasonality in data.

This training focuses on using the KNIME Analytics Platform for time series analysis, covering data preprocessing, feature engineering, model selection, and advanced forecasting techniques. Participants will gain hands-on experience in building and deploying time series models for real-world applications. By the end of the course, learners will have a deep understanding of how to use KNIME for time-dependent data analysis and predictive modeling.

Prerequisites

• Basic understanding of data analytics and machine learning concepts.
• Familiarity with KNIME workflows (recommended but not mandatory).
• Knowledge of Python or R for advanced modeling (optional).

Table of Contents

1. Introduction to Time Series Analysis

1.1 Understanding time-dependent data
1.2 Time series components: trend, seasonality, cyclicality, and noise
1.3 Real-world applications of time series analysis
1.4 Introduction to time series analysis in KNIME

2. Data Preprocessing for Time Series

2.1 Importing time series data into KNIME(Ref: L4-TS: Time Series Analysis in KNIME Analytics Platform)
2.2 Handling missing values and outliers
2.3 Time-based aggregation and transformations
2.4 Resampling and frequency conversion

3. Exploratory Data Analysis (EDA) for Time Series

3.1 Visualizing time series data in KNIME
3.2 Identifying seasonality, trends, and stationarity
3.3 Rolling statistics and moving averages
3.4 Autocorrelation and Partial Autocorrelation Functions (ACF & PACF)

4. Feature Engineering for Time Series Models

4.1 Creating lag features and rolling windows
4.2 Encoding time-based variables
4.3 Fourier transforms and spectral analysis
4.4 Handling multivariate time series

5. Time Series Forecasting Models

5.1 Introduction to forecasting techniques
5.2 ARIMA (AutoRegressive Integrated Moving Average) modeling
5.3 Exponential Smoothing and Holt-Winters models
5.4 Seasonal decomposition of time series

6. Machine Learning for Time Series

6.1 Regression-based forecasting models
6.2 Decision Trees and Random Forest for time series
6.3 LSTM and deep learning approaches (optional)
6.4 Comparing traditional vs. ML-based time series forecasting

7. Model Evaluation and Selection

7.1 Performance metrics for time series models (RMSE, MAPE, MAE)
7.2 Cross-validation techniques for time series
7.3 Hyperparameter tuning and model optimization
7.4 Selecting the best forecasting model

8. Deploying Time Series Models in KNIME

8.1 Automating forecasting workflows
8.2 Real-time prediction using KNIME Server
8.3 Integrating time series models with external APIs
8.4 Scaling time series models for production environments

9. Case Studies and Practical Applications

9.1 Financial market trend forecasting
9.2 Sales and demand prediction
9.3 Energy consumption modeling
9.4 End-to-end time series forecasting project

Conclusion

This course equips participants with the knowledge and hands-on experience to perform effective time series analysis using the KNIME Analytics Platform. By mastering techniques from traditional statistical models to machine learning-based forecasting, learners will be able to extract valuable insights from time-dependent data. The training provides practical applications across industries, ensuring that participants can apply their skills to real-world business problems.