Supply Chain Data Analytics (SCDA) Summary plus formula sheet notes
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Course
Supply Chain Data Analytics (E_TSCM_SCDA)
Institution
Vrije Universiteit Amsterdam (VU)
Book
Practical Time Series Forecasting with R
This document contains a profound overview of most course material relevant to the (resit) exam of Supply Chain Data Analytics (E_TSCM_SCDA), a course provided by Vrije Universiteit Amsterdam for the study MSc Transport & Supply Chain Management. The file covers lecture material, tutorial notes (r...
Business Administration: Transport and Supply Chain Management
Supply Chain Data Analytics (E_TSCM_SCDA)
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Supply Chain Data Analytics
overview
Table of Contents
Forecasting and Smoothing ............................................................................................... 2
Lecture 1 – Introduction and Forecasting Process ....................................................................... 2
The Forecasting Process (steps) .................................................................................................................... 2
Choosing Forecasting Methods .................................................................................................................... 5
Visualizing time series ................................................................................................................................... 6
Overfitting and underfitting.......................................................................................................................... 7
Lecture 2 – Smoothing Methods ................................................................................................ 8
Lecture 3 – Smoothing Methods part 2 .................................................................................... 11
Regression Methods ........................................................................................................ 12
Lecture 4 – Regression Methods (part 1) .................................................................................. 12
Lecture 5 – Regression Methods 2 ............................................................................................ 15
Tutorial 3 ................................................................................................................................. 15
Time Series Regression..................................................................................................... 16
Lecture 6 – Time Series Regression (part 1) .............................................................................. 16
Lecture 7 – Time Series Regression (Part 2) .............................................................................. 18
Classification and Clustering ............................................................................................ 23
Lecture 8 – Classification .......................................................................................................... 23
Lecture 9 – Clustering............................................................................................................... 29
Decision Trees .................................................................................................................. 30
Lecture 10 – Decision Trees ...................................................................................................... 30
SCDA Formula sheet Explained......................................................................................... 37
What is the difference between Mean Absolute Error and Mean Squared Error? .................................... 37
What is the difference between Centered Moving average and moving average? ................................... 37
How do you calculate the Trailing Moving Average with window size w? ................................................. 38
MatthNotes 1
,Forecasting and Smoothing
Includes:
• Lecture 1 – Introduction and Forecasting Process
• Lecture 2 – Smoothing Methods (part 1)
• Lecture 3 – Smoothing Methods (part 2)
Lecture 1 – Introduction and Forecasting Process
Data Analytics – The process of exploring and analyzing large datasets to find hidden
patterns, trends, and possible correlations.
Goal: to extract value (insight) out of data and make decisions (improve decision making,
better customer service)
Steps:
1. Understanding the problem – understand, define goals, plan for a solution
2. Data collection – gather right data from various source
3. Data cleaning – make data ready for analysis by removing unwanted, redundant, and
missing values (one of the most time consuming)
4. Data exploration and analysis – using tools and techniques such as data visualization
and data mining to analyze data
5. Interpreting the results – to find hidden patterns, future trends, insights
Data Analytics Techniques
1. Forecasting
2. Regression
3. Time series
4. Classification
5. Machine learning
6. Clustering
The Forecasting Process (steps)
- Cross sectional data (prediction): type of data collected by observing many subjects
(such as individuals, firms, countries, or regions) at one point in time.
- Time series (forecasting): simply a series of data points ordered in time. In time
series, time is often the independent variable, and the goal is usually to make a
forecast for the future
What is the difference between prediction and forecasting?
Forecasting process steps
1. The forecasting goal
- How will the forecast be used by the organization?
- What type of forecasts are needed?
- Is it descriptive or predictive? To find seasonal patterns and trends in the current data
or to forecast future values
- Forecast horizon? Updating? How far into the future should we forecast, how often
can we update the forecast?
- Automated or manual?
MatthNotes 2
, 2. Getting data
- What type of data do we need?
- Data quality
- Single or multiple sources of data – combine from different sources for better results
- Temporal frequency of data
- Granularity of data – region
- *Domain expertise may help with this
3. Data exploring and visualization
- Goal: detect initial patterns and potential problems such as extreme or missing values
- View the data (Excel or R)
- Visualize the data
- Use your observations to pre-process and to choose forecasting methods
4. Pre-processing
- Why do we need to pre-process data? Possible reasons: missing values, obsolete or
redundant fields, unequally spaced series, values not consistent with policy or
common sense
- Handling missing data – Possibilities: ignore it, omit it, replace with some constant,
the mean, median or mode, or randomly generated values from the distribution,
impute the missing value based on other characteristics of the data.
When missing data, there are two possible forecasting: Naïve or Seasonal Naïve Forecasting
You may want to remove variables or records that do not help with the forecast, you could
remove unary variables or duplicate records.
5. Partition series
- It is done to avoid overfitting.
- Overfitting: performs well on the training data, but poor on the validation set. The
model is unable to generalize the data.
-
- Data partitioning:
MatthNotes 3
, i. Fit the models only to the training period
ii. Assess performance on the validation period
iii. Deploy the “best” model for future forecasts. Rerun the model on the
entire series; use the model to forecast the future
- Choosing a training validation period. How would you choose the length of the
training and validation period? Depends on forecasting goal
6. Apply Forecasting Methods
- What kind of forecasting method? Data-driven, model-based, or judgmental?
- How many different methods we are going to test?
- Are we going to combine methods?
- Data driven methods
i. Learn patterns from the data
ii. Requires less user input and are easily automated, but a large time
series is necessary for adequate learning
iii. Possible methods: smoothing methods
- Model-based methods
i. Use statistical, mathematical, or a scientific model to approximate a
time series
ii. Suitable for time series with limited data and can include external
information
iii. Possible methods: regression methods
- Judgmental methods
i. Incorporate intuitive judgment, opinions, and subjective probability
estimates
ii. Are used when there is a lack of good data, or to adjust statistical
forecast, or to compare and combine with statistical forecasts.
- Combining methods and ensembles
i. Combining multiple forecasts can improve predictive performance
1. Two-level forecast: method 1 generates initial forecasts,
method 2 uses the forecast errors from method 1 to forecast
future errors
2. Ensembles: apply multiple forecasting methods, average the
different forecasts, creating forecasts that are more robust and
of a higher precision.
7. Evaluate and compare performance
- Missing
8. Implement forecast/system
MatthNotes 4
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