Introduction to the Course
  • Welcome to Time Series Analysis with Python
  • Data and Scripts Used in the Course
  • Introduction to the Python Data Science Environment
  • Installation Instructions For Mac
Read in Data From External Data Sources
  • Introduction to Pandas
  • Read in CSV Data
  • Read in Excel Data
  • Read in HTML Data
  • Read in JSON Data
Preprocessing & Visualising Time Series Data in Python
  • Some Date Specific Python Functions
  • An Example of Time Series Data in Python
  • More Details on Datetime
  • Basic Operations on Time Series Data
  • Theory Behind Exploratory Data Analysis (EDA)
  • Principles of Data Visualization
  • Prep Up the Time Series Data
  • Line Charts For Examining Temporal Data
  • Multiple Lines in the Same Chart
  • Aggregating & Visualising Data Summary
  • Using Multiple Line Plots For Discerning Specific Information
  • Histograms
  • Plot the Temporal Variations of Two Entities
Characteristics & Conditions of Time Series Data
  • Moving Average (MA) Forecast Example
  • Classical Time Series Data
  • Different Components of Time Series Data
  • Seasonal Part of Time Series
  • Of Multiplicative and Additive Seasonality
  • Testing for Stationarity: ADF Test
  • Make Time Series Stationary: Take Log
  • First Order Differencing to Make Time Series Stationary
  • Log Based Differencing
  • Linear Regression For Detrending
Basic Time Series Forecasting
  • Rolling Mean For Detecting Temporal Variation
  • Simple Exponential Smoothing (SES)
  • Holt extended simple exponential smoothing
  • Holt Winters
  • Auto Regression Model (AR): Consider Previous Time Steps
  • Implement a Basic ARIMA Model
  • Automated ARIMA & Account for Seasonality (SARIMA)
Machine Learning For Time Series
  • Random Forest For Identifying Important Time Periods
  • "Prophetic" Time Series Forecasting
  • Using Prophet For Predicting Values for a Future Time Frame
Use Deep Learning For Time Series Data
  • What is Keras?
  • Install Keras on Windows
  • Install Keras on Mac
  • Theory Behind ANN and DNN
  • MLP For Time Series
  • LSTM For Time Series Data
  • LSTM For Predicting Stock Prices
  • Univariate LSTM For Stock Prediction