Introduction
Task 1: Compute Candlestick Data
To complete this task, you need to be able to compute candlestick data from the temperature data for a particular location in Europe between 1980-2019. Here is an example of the fields needed for candlestick data:
Candlestick Data Table
Date | Open | High | Low | Close |
---|---|---|---|---|
1980-01-01 | -1.249 | 20 | -3 | -1.200 |
1981-01-01 | -1.200 | 25 | -2 | -1.300 |
1982-01-01 | -1.300 | 27 | -1 | -1.400 |
1983-01-01 | -1.400 | 28 | -1 | -1.350 |
1984-01-01 | -1.350 | 24 | 0 | -1.450 |
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To compute the candlestick data, the following logic is used:
- Open: The average mean temperature per time frame (i.e., year) in the previous time frame.
- Close: The average mean temperature per unit in this time frame (same as Open, but for the current time frame).
- High: The highest temperature value seen this time frame.
- Low: The lowest temperature value seen this time frame.
The candlestick data should be computed using a function which returns a vector of std::vector<Candlestick>
objects. The Candlestick
class should be defined to represent this data.
Task 2: Create a Text-Based Plot of the Candlestick Data
The second task involves creating a text-based plot of the candlestick data (i.e., daily, monthly, or yearly). Here is an example of what the plot might look like:
Example: | | | ---| --|----|-- --|----|--
We recommend starting by manually creating the plot using text characters in a text editor, using characters like -
for the top of a box and |
for the stalk of the candlestick. This visual representation should show temperature data in a text format.
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Task 3: Filter Data and Plotting Using Text
In this task, you need to provide at least two filter options for data (such as by date range, country, and temperature data range) and plot the filtered data using text-based characters.
Task 4: Predicting Data and Plotting
Task 4 involves predicting temperature changes for a selected date range and country. You will need to develop prediction functions of your choice that calculate these values from the provided historical data.
Provide a brief description of the prediction function used, including a code fragment, calculation method, and a description of the results.
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What to Submit
- A PDF file containing all code (concatenate .cpp and .h files into a single text file and save as a PDF).
- A PDF file containing your report, describing how each task was carried out with screenshots of results/output.
- A 3-minute system demonstration video with voice narration showing key features and code logic.
Marking Criteria
- Code style: indentation, descriptive comments – 6 marks
- TASK 1: Compute candlestick data CODE – 10 marks
- TASK 1: Compute candlestick data DESCRIPTION – 6 marks
- TASK 2: Create a text-based plot of the candlestick data CODE – 10 marks
- TASK 2: Create a text-based plot of the candlestick data DESCRIPTION – 6 marks
- TASK 3: Filtering option and plot a text graph CODE – 10 marks
- TASK 3: Filtering option and plot a text graph DESCRIPTION – 6 marks
- TASK 4: Predicting data and plotting with a chosen model CODE – 10 marks
- TASK 4: Predicting data and plotting with a chosen model justification and DESCRIPTION – 6 marks
- Originality and challenge of implementation – 10 marks
- Clearly label all sections of the code that you personally wrote without assistance – 6 marks
- Submit correct items: code as text in PDF, report as PDF, ZIP file for code & video – 4 marks
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