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Personal Information

  • Case Western Reserve University Class of 2019
  • Applied Mathematics and Computer Science Major

Weekly Log

Week 0 (30 May - 2 Jun)

  • Met Dr. Povinelli 4 times: to discuss possible project topics, to get oriented with the lab, to decide on a project topic, and to establish weekly milestones
  • Obtained MU and MSCS account logins and ID card
  • Read most of "Learning Deep Architectures for AI"
  • Read section on Sequence Modeling (Ch 10) of The Deep Learning book
  • Read various other papers

Week 1 (5 Jun - 9 Jun)

  • Attended GasDay camp and learned about what GasDay does
  • Attended responsible conduct of research training
  • Continued to read papers
  • Decided to use TensorFlow and Keras for now
  • Got Anaconda, TensorFlow, and Keras installed on a couple lab computers
  • Learned how to access customer data

Week 2 (12 Jun - 16 Jun)

  • Started playing with TensorFlow, mostly using the GEFCom2014-E dataset to allow me to continue working when not at the lab
  • Things I got working in Tensorflow:
    • n-layer Sequence to sequence (seq2seq) model (encoder-deocder architecture)
    • Autoregressive seq2seq model (using slower ops)
    • Multilayer Perceptron
    • 1D ConvNet on weather inputs, in parallel with MLP to process load and time inputs
  • Will try ConvNet on all 3 inputs, feeding into RNN, with the seed state being the month, day, and day of week at the initial timestamp
  • Will try Clockwork RNN

Week 3 (19 Jun - 23 Jun)

  • Reinstalled CUDA
  • Started training seq2seq models on GasDay (electric) data
  • Got TensorBoard to work
  • Trained AR LSTM on GasDay gas data

Week 4 (26 Jun - 30 Jun)

  • Continued to experiment with hyperparameters
  • Achieved good performance on both gas and electricity datasets
  • Prepared a presentation of my research for other REU students (slides)
  • Attempted to train a model with objective that was not at all convex; it failed miserably
  • Still can't figure out why Dropout makes everything worse

Week 5 (3 Jul - 7 Jul)

  • Continued to experiment with different models
  • Incorporated both long- and short-term dependencies into an encoder-decoder architecture
  • Presented to GasDay information about what I have been doing and neural networks in general (slides).

Week 6 (10 Jul - 14 Jul)

  • Did not finish draft of paper by deadline
  • Worked on determining the best model
  • Reincorporated BatchNorm and DropOut with good results
  • Experimented with convolution layers, but they rapidly overfit data

Week 7 (17 Jul - 21 Jul)

  • Started creating poster
  • Tried to evaluate models on test data, but results were highly subpar
  • Spent the rest of the week trying to figure out why errors on test data were inconsistent with val errors
    • It is probably because the model is trained on years 0-14, validated on 14-18, and tested on 18-20
    • 4 years skip between training data and test might cause issues
    • Tried using detrended data, but results did not improve significantly

Week 8 (24 Jul - 28 Jul)

  • Finished poster (pdf here)
  • Finalized models for gas forecasting. I decided to not use GasDay's electricity data at all.
  • Started running models on electric data from GefCom 2014 so that results may be reproduced.
  • Finished first draft of paper (which I may try to submit to a conference)
  • Presented my work to GasDay

Week 9 (31 Jul - 4 Aug)

  • Gave draft to Dr. Povinelli for feedback
  • Presented to REU students
  • Poster session
  • Continued to tune hyperparameters on GefCom data
  • Finished Responsible Conduct of Research training


Here are some of the papers I have read, skimmed, and partially read:

Forecasting with Deep Learning

Hybrid Methods

Review Papers

LSTMs, Training, and Possible Improvements