Row 96254

Row ID: 96254 | Dataset Entry | Axioma AXP Content Repository

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Geospatial Trend Analysis Question

What is your guess…

I have built a large number of market prediction (domain specific) machine learning models over the past year. My current project involves utilizing geospatial data and transfer learning to analyze Google searcg trends for different stocks. The way I am able to train on limited data is through the use of a synthetic data augmentation process I designed to capture time-scale invariance. One piece of advice if you try to recreate this approach is to use batch training to avoid overfitting especially while transfer learning. (If any of that sounds confusing or dumb please just ignore it because specifics are not necessarily that important.)

Currently, I am analyzing data from major cities in the US (including D.C.) and large cities internationally such as Moscow and Beijing. I am curious which city people would assume creates the most accurate model. Which city’s search data is best for predicting future share prices? There are no wrong answers, but I am looking for more cities to add into my model while trying to be cognizant of the size because all of this is done on a ~5 year old laptop. I will note that I am using a pseudo SQL library to manage a database and be (semi) respectful when doing large data pulls.

FieldValue
text Geospatial Trend Analysis Question What is your guess… I have built a large number of market prediction (domain specific) machine learning models over the past year. My current project involves utilizing geospatial data and transfer learning to analyze Google searcg trends for different stocks. The way I am able to train on limited data is through the use of a synthetic data augmentation process I designed to capture time-scale invariance. One piece of advice if you try to recreate this approa…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-25
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Raw Record

{
  "text": "Geospatial Trend Analysis Question\n\nWhat is your guess…\n\nI have built a large number of market prediction (domain specific) machine learning models over the past year. My current project involves utilizing geospatial data and transfer learning to analyze Google searcg trends for different stocks. The way I am able to train on limited data is through the use of a synthetic data augmentation process I designed to capture time-scale invariance. One piece of advice if you try to recreate this approach is to use batch training to avoid overfitting especially while transfer learning. (If any of that sounds confusing or dumb please just ignore it because specifics are not necessarily that important.)\n\nCurrently, I am analyzing data from major cities in the US (including D.C.) and large cities internationally such as Moscow and Beijing. I am curious which city people would assume creates the most accurate model. Which city’s search data is best for predicting future share prices? There are no wrong answers, but I am looking for more cities to add into my model while trying to be cognizant of the size because all of this is done on a ~5 year old laptop. I will note that I am using a pseudo SQL library to manage a database and be (semi) respectful when doing large data pulls.",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
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}
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