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Time Series Forecasting using Auto ARIMA & Deep Learning Algorithms

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In this repository, I'll :

• Implement some deep learning algorithms for forecasting, such as MLP, CNN, and LSTM-based approaches. (All Univariate Models and DL Forecast files)
• Provide an example of forcasting using both MLP and CNN algorithms using data from yfinance. (CNN and MLP notebooks)

•Outline:

• Gathering the data
• Look at the data
• Data stationarity analysis
  • Test for Stationarity
  • Apply some data transformation for Stationarity
• TS Decomposition
• See the Correlation between Our Time Series.
• Plot the ACF & PACF For the data.
• Forecasting
  • Auto ARIMA model
  • Deep Learning Algorithms
    • MLP approaches
      • Univariate Forecasting
      • Multivariate Forecasting
        • Multiple Input
          • Single Dense
          • Multi-headed
        • Multiple Parallel
          • Vector-Output
          • Multi-Output
      • Multi-Step Forcasting
        • Multiple Input
        • Multiple Parallel
  • CNN
    • Univariate Forecasting
    • Multivariate Forecasting
      • Multiple Input
        • Multi-headed
      • Multiple Parallel
        • Vector-Output
        • Multi-Output
    • Multi-Step Forcasting
      • Univariate Multi-Step
      • Multivariate Multi-Step
        • Multiple Input
        • Multiple Parallel