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Course - Time Series Analysis

https://365datascience.teachable.com/p/time-series-analysis-in-python

https://365datascience.teachable.com/courses/enrolled/666338

https://www.toptal.com/artificial-intelligence/retail-sales-forecasting-ai

Introduction to Time Series in Python​

  • Start Introduction to Time Series Data
  • Start Notation for Time Series Data
  • Start Peculiarities
  • Start Loading the Data
  • Start Examining the Data
  • Start Plotting the Data
  • Start The QQ Plot

Creating a Time Series Object in Python​

  • Start Transforming String inputs into DateTime Values
  • Start Using Dates as Indices
  • Start Setting the Frequency
  • Start Filling Missing Values
  • Start Adding and Removing Columns in a Data Frame
  • Start Splitting up the Data
  • Start Appendix: Updating the Dataset

Working with Time Series in Python​

  • Start White Noise
  • Start Random Walk
  • Start Stationarity
  • Start Determining Weak Form Stationarity
  • Start Seasonality
  • Start Correlation Between Past and Present Values
  • Start The ACF
  • Start The PACF

Picking the Correct Model​

Start A Quick Guide to Picking the Correct Model

The Autoregressive Model​

  • Start The AR Model
  • Start Examining the ACF and PACF of Prices
  • Start Fitting an AR(1) Model for Index Prices
  • Start Fitting Higher Lag AR Models for Prices
  • Start Using Returns
  • Start Examining the ACF and PACF of Returns
  • Start Fitting an AR(1) Model for Index Returns
  • Start Fitting Higher Lag AR Models for Returns
  • Start Normalizing Values
  • Start Model Selection for Normalized Returns
  • Start Examining the AR Model Residuals
  • Start Unexpected Shocks from Past Periods

The Moving Average (MA) Model​

  • Start The MA Model
  • Start Fitting an MA(1) Model for Returns
  • Start Fitting Higher-Lag MA Models for Returns
  • Start Examining the MA Model Residuals for Returns
  • Start Model Selection for Normalized Returns
  • Start Fitting an MA(1) Model for Prices
  • Start Past Values and Past Errors

The Autoregressive Moving Average (ARMA) Model​

  • Start The ARMA Model
  • Start Fitting a Simple ARMA Model for Returns
  • Start Fitting a Higher-Lag ARMA Model for Returns - part 1
  • Start Fitting a Higher-Lag ARMA Model for Returns - part 2
  • Start Fitting a Higher-Lag ARMA Model for Returns - part 3
  • Start Examining the ARMA Model Residuals of Returns
  • Start ARMA for Prices
  • Start ARMA Models and Non-stationary Data

The Autoregressive Integrated Moving Average (ARIMA) Model​

  • Start The ARIMA Model
  • Start Fitting a Simple ARIMA Model for Prices
  • Start Fitting a Higher Lag ARIMA Model for Prices - part 1
  • Start Fitting a Higher Lag ARIMA Model for Prices - part 2
  • Start Higher Levels of Integration
  • Start Using ARIMA Models for Returns
  • Start Outside Factors and the ARIMAX Model
  • Start Seasonal Models - the SARIMAX Model
  • Start Predicting Stability

The ARCH Model​

  • Start The ARCH Model
  • Start Volatility
  • Start A More Detailed Look of the ARCH Model
  • Start The arch_model Method
  • Start The Simple ARCH Model
  • Start Higher Lag ARCH Models
  • Start An ARMA Equivalent of the ARCH Model

The GARCH Model​

  • Start The GARCH Model
  • Start The ARMA and the GARCH
  • Start The Simple GARCH Model
  • Start Higher-Lag GARCH Models
  • Start An Alternative to the Model Selection Process

Auto ARIMA​

  • Start Auto ARIMA
  • Start Preparing Python for Model Selection
  • Start The Default Best Fit
  • Start Basic Auto ARIMA Arguments
  • Start Advanced Auto ARIMA Arguments
  • Start The Goal Behind Modeling

Forecasting​

  • Start Introduction to Forecasting
  • Start Simple Forecasting (Returns with AR and MA)
  • Start Intermediate Forecasting (MAX Models)
  • Start Advanced Forecasting (Seasonal Models)
  • Start Auto ARIMA Forecasting
  • Start Pitfalls of Forecasting
  • Start Forecasting Volatility
  • Start Appendix: Multiple Regression Forecasting

Business Case​

Start Business Case - A Look Into the Automobile Industry

Time Series Forecasting on Stock Prices

Time Series Made Easy in Python - darts documentation​

darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, usingfit()andpredict()functions, similar to scikit-learn. The library also makes it easy to backtest models, and combine the predictions of several models and external regressors. Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on multiple time series, and some of the models offer probabilistic forecasts