An introduction of developing and application time series forecast models with both traditional time series methods and machine learning techniques. Case study for a challenging very short-term electrical price forecasting project was presented.

1. Time Series Forecasting
with Machine Learning
See the future with a crystal ball
Dr Wei Liu
Data Science Lead

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What is forecasting

4. Time Series and its Forecasting
• A time-series is a set of observations on a quantitative variable collected over
time.
• Examples
• Stock: Dow Jones Industrial Averages
• Marking: sales, inventory, and customer counts etc
• Economics: Interest rates, GDP, and employment etc.
• Energy (Electricity, Gas, Oil, and Solar) demands and prices etc.
• Weather: e.g., local and global temperature etc.
• Sensors: Internet-of-Things
• Businesses are often very interested in forecasting time series variables.
• In time series analysis, we analyze the past behavior of a variable in order to
predict its future behavior
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5. Approaches for Time Series Forecasting
Classical Time Series Analysis Methods
• Naïve, SNaïve
• Seasonal decomposition (+ any model)
• Exponential smoothing
• ARIMA, SARIMA
• GARCH
• Dynamic linear models
• TBATS
• Prophet
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Machine Learning Methods
• Generalised Linear Modelling (GLM)
• Gradient Boost Machine (GBM)
• Random Forest (DF)
• Deep Learning (DL)
• Automated Machine Learning (AutoML)

6. The Classical Approaches: Pros and Cons
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7. The Classical Approaches: Notable Disadvantages
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8. Machine Learning Approaches: Pros and Cons
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9. What we have done with time series forecast
modelling?
A Real and Challenging Project
Our clients engages us to develop a short-range electricity price forecasting tool for optimising their operation/production. The problem
is quite challenging as the data is really dynamic with rapid variations without clear trends and seasonality.
Our solution is to investigate intensively with available classic and ML approaches for the problem and identify the best approach with
the most accurate forecastings.
The accuracies of less than 10% for forecasting electricity price 24 hours in 5-minute interval in advance with the best model identified
has achieved.
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10. Forecasting Examples
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11. Forecasting Examples
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12. Forecasting Examples
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13. Forecasting Examples
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14. Forecasting Examples
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15. What we have done with time series forecast
modelling?
A powerful Cloud-Computing Tool for Time Series Forecast Modelling
• Easy to use with interactive graphic user interface
• Interactive data exploring and visualization
• Process and prepare data for forecast modelling
• State-of-the-art classical and machine learning time series forecasting algorithms.
• Automatically tuning learning parameters using repeated cross-valdidation.
• Benchmark experiments with different models and measures
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16. More Forecasting Tool Screenshots
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17. More Forecasting Tool Screenshots
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18. More Forecasting Tool Screenshots
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19. More Forecasting Tool Screenshots
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20. What we have done with time series forecast
modelling?
A Production Level Interactive Tool for
Implementing Time Series Forecasting Models
• Load input data from multiple sources (csv data file,
Google Spreadsheet and cloud database)
• Carrying out forecasting analysis with high-
performance cloud computing server
• Interactive view of forecasting results
• Export and download forecasting results
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21. What we have done with time series forecast
modelling?
A Production Level Interactive Tool for Implementing Time Series Forecasting Models
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22. Use cases for future opportunities
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