Neural networks for forecasting
by Richard Heimann, Nathan Danneman, Pradeepta Mishra, Bater Makhabel
R: Mining Spatial, Text, Web, and Social Media Data
- R: Mining Spatial, Text, Web, and Social Media Data
- Table of Contents
- R: Mining Spatial, Text, Web, and Social Media Data
- R: Mining Spatial, Text, Web, and Social Media Data
- Credits
- Preface
- 1. Module 1
- 1. Warming Up
- 2. Mining Frequent Patterns, Associations, and Correlations
- An overview of associations and patterns
- Market basket analysis
- Hybrid association rules mining
- Mining sequence dataset
- The R implementation
- High-performance algorithms
- Time for action
- Summary
- 3. Classification
- Classification
- Generic decision tree induction
- High-value credit card customers classification using ID3
- Web spam detection using C4.5
- Web key resource page judgment using CART
- Trojan traffic identification method and Bayes classification
- Identify spam e-mail and Naïve Bayes classification
- Rule-based classification of player types in computer games and rule-based classification
- Time for action
- Summary
- 4. Advanced Classification
- 5. Cluster Analysis
- 6. Advanced Cluster Analysis
- Customer categorization analysis of e-commerce and DBSCAN
- Clustering web pages and OPTICS
- Visitor analysis in the browser cache and DENCLUE
- Recommendation system and STING
- Web sentiment analysis and CLIQUE
- Opinion mining and WAVE clustering
- User search intent and the EM algorithm
- Customer purchase data analysis and clustering high-dimensional data
- SNS and clustering graph and network data
- Time for action
- Summary
- 7. Outlier Detection
- Credit card fraud detection and statistical methods
- Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
- Intrusion detection and density-based methods
- Intrusion detection and clustering-based methods
- Monitoring the performance of the web server and classification-based methods
- Detecting novelty in text, topic detection, and mining contextual outliers
- Collective outliers on spatial data
- Outlier detection in high-dimensional data
- Time for action
- Summary
- 8. Mining Stream, Time-series, and Sequence Data
- 9. Graph Mining and Network Analysis
- 10. Mining Text and Web Data
- A. Algorithms and Data Structures
- 2. Module 2
- 1. Data Manipulation Using In-built R Data
- What is data mining?
- Introduction to the R programming language
- Data type conversion
- Sorting and merging dataframes
- Indexing or subsetting dataframes
- Date and time formatting
- Creating new functions
- Loop concepts - the for loop
- Loop concepts - the repeat loop
- Loop concepts - while conditions
- Apply concepts
- String manipulation
- NA and missing value management
- Missing value imputation techniques
- Summary
- 2. Exploratory Data Analysis with Automobile Data
- Univariate data analysis
- Bivariate analysis
- Multivariate analysis
- Understanding distributions and transformation
- Interpreting distributions
- Variable binning or discretizing continuous data
- Contingency tables, bivariate statistics, and checking for data normality
- Hypothesis testing
- Non-parametric methods
- Summary
- 3. Visualize Diamond Dataset
- 4. Regression with Automobile Data
- 5. Market Basket Analysis with Groceries Data
- 6. Clustering with E-commerce Data
- 7. Building a Retail Recommendation Engine
- 8. Dimensionality Reduction
- 9. Applying Neural Network to Healthcare Data
- 1. Data Manipulation Using In-built R Data
- 3. Module 3
- 1. Going Viral
- 2. Getting Started with R
- 3. Mining Twitter with R
- 4. Potentials and Pitfalls of Social Media Data
- 5. Social Media Mining – Fundamentals
- Key concepts of social media mining
- Good data versus bad data
- Understanding sentiments
- Sentiment polarity – data and classification
- Supervised social media mining – lexicon-based sentiment
- Supervised social media mining – Naive Bayes classifiers
- Unsupervised social media mining – Item Response Theory for text scaling
- Summary
- 6. Social Media Mining – Case Studies
- A. Conclusions and Next Steps
- Bibliography
- Index
Neural networks can also be used to generate forecasts for a time series variable. There is a library called forecast in R that deploys feed-forward neural networks with a single hidden layer, and lagged inputs for forecasting univariate time series. For the forecasting example, we have taken an inbuilt dataset available in R called "Air Passengers" to apply the neural network.
The following table reflects the parameter arguments required by the nnetar function with the corresponding description of how they are being used in the model:
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Univariate time series with a time variable |
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Number of non-seasonal lags used as input |
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Number of seasonal lags used as input |
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Number of nodes in the hidden layer |
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Number of networks to fit with different random settings of weights |
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This is known as box-cox transformation parameter |
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External regressors used in fitting the model |
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Point forecasts as mean |
The actual time series looks as follows:
> fit<-nnetar(AirPassengers, p=9,P=,size = 10, repeats = 50,lambda = 0) > plot(forecast(fit,10))
A neural-network-based forecasting model generates the following results as an output:
> summary(fit) Length Class Mode x 144 ts numeric m 1 -none- numeric p 1 -none- numeric P 1 -none- numeric scale 1 -none- numeric size 1 -none- numeric lambda 1 -none- numeric model 50 nnetarmodels list fitted 144 ts numeric residuals 144 ts numeric lags 10 -none- numeric series 1 -none- character method 1 -none- character call 6 -none- call
With a forecast of the next 10 periods, the graph looks as follows:
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