Mihaela Grigore
  • 👋About
  • 👩‍🏭Personal projects
    • Computer Vision | Deep Learning with Tensorflow & Keras (ResNet50, GPU training)
    • Computer Vision | Convolutional Neural Networks with PyTorch
    • Computer Vision | Facial Recognition with Keras, FaceNet, Inception, Siamese Networks
    • NLP | Topic modeling on tweets
    • NLP | Sentiment analysis of tweets: TextBlob, VADER and Flair
    • Time series | Exploration on Crypto price dataset
    • Data scraping | Social Media Scraping: Twitter Developer API for Academics
    • Data Scraping | Collecting historical tweets without Twitter API
  • ✍️Notes
    • Machine Learning in Production
      • Feature transforms
      • Feature selection
      • Data journey
    • NLP
      • Information Retrieval
    • Computer Vision
    • Time series
      • Stationarity
    • Data
      • Labeling
    • Python
      • ndarray slicing with index out of bounds
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    • Computer Vision
      • Selection of research articles
    • NLP
      • Handwriting Text
      • Information Retrieval
      • Mono- / multilingual
      • Topic Modeling
      • Language Models
    • Time Series
    • Generative Adversarial Netoworks (GAN)
    • Python
      • Python basics
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Time series | Exploration on Crypto price dataset

PreviousNLP | Sentiment analysis of tweets: TextBlob, VADER and FlairNextData scraping | Social Media Scraping: Twitter Developer API for Academics

Last updated 3 years ago

I wanted to see a notebook / tutorial that would take me through the basics of working with time series.

Most notebooks I saw were either not very rigorous or they took me straight into price prediction using some methods, which is not what I needed right away.

So I wrote my own introductory notebook. I acquired most of the information I used here through reading Introduction to Time Series Forecasting With Python by Jason Brownlee.

See

What this project is:

  • a good starting point for understanding time series data and how it differs from problems with other type of tabular data

  • a cookbook to use for exploration when starting to work with a new dataset

What this project is not:

  • it is not about prediction. It stops at exploration and understanding the data.

  • it's not meant for advanced practitioners of asset price prediction - unless you want to revisit some concepts.

Contents

  1. Quick overview

  2. Dataset description

  3. Basic trading data visualization

  4. Preprocessing

  5. Feature engineering

  6. Typical time series visualizations

  7. Power transforms

  8. Temporal structure of time series data

  9. Model evaluation

👩‍🏭
project repository on GitHub.