landmann / trend-detection

Trend detection using ML and Traditional Vision Techniques. Done as part of 6.869, MIT's Computer Vision Course.

Towards Graph Trend Understanding

Dataset

Input types:

• Single line graphs

Outputs (Labels)

• Increase
• Decrease
• Neutral

To obtain actual data, please post an issue on this repo and we'll send it your way!

Contributions

1. Curate a dataset containing 125,000 graphs of varying styles, functions, and trends using MatPlotlib.

   • Graph functions include linear, logarithmic, polynomial, power, exponential.
   • Trends include stable, ascending, descending, and variable.

2. Create a multi-class classifier to infer the type of trend within a line graph. Two techniques done: machine learning using a ResNet-34 in PyTorch, and traditional methods for visual feature extractions.

Further Steps

• Process graphs with multiple time-series data (multiple lines).

• Infer trend from a scatterplot.

• Infer maxima and minima in a graph.

• Include OCR to parse through title and axis labels and generate an actual coherent title.

Examples of graphs

The following graphs are labeled 'increasing', 'neutral', and 'decreasing', respectively. These were generated by the code found within this repo.

Test set contains images images scraped from the Internet by Googling. They come with a lot more variation and noise. These were manually labeled by us to maintain consistency.

References

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