• Python programing
• Working knowledge of Pandas and Sklearn
• Basic statistics

• Define time series
• Explain why time series analysis is important
• Identify time series applications
• Explain how to decompose trend, seasonality, and residuals
• Explain how to decompose additive, multiplicative, pseudo-additive models
• Use Python to create a time series forecasting model

• Introduction to time series
• Applications of time series
• Examples of time series
• Time series decomposition: Trends, Seasonality, Residuals
• Time series decomposition models: Additive, Multiplicative, Pseudo-Additive

• Define stationarity
• Explain how to transform nonstationary time series data
• Describe methods for determining stationarity
• Use Python identify nonstationary time series data

• What is stationarity?
• Why is stationarity important?
• Mathematical transformations: differencing, detrending, logarithms
• Determining stationarity: visualization, gaussian distribution, summary statistics, statistical tests

• Explain the need for data smoothing
• List common data smoothing techniques
• Explain how common data smoothing techniques work
• Use Python to smooth time series data

• Naive Implementation
• Simple Average
• Moving Average
• Weighted Moving Average
• Single Exponential Smoothing
• Double Exponential Smoothing
• Triple Exponential Smoothing (Holt-Winters)

• Define autocorrelation
• Describe the autocorrelation and partial autocorrelation functions
• Explain how autoregressive and moving average models work
• Use Python to build autocorrelation models

• Autocorrelation Function (ACF)
• Partial Autocorrelation Function (PACF)
• Autoregressive Models (AR)
• Moving Average Models (MA)
• Identifying AR or MA model

• Explain how ARMA, ARIMA, and SARIMA models work
• Describe how to determine the order of p and q
• List common guidelines for building ARMA and ARIMA models
• Use Python to implement ARMA, ARIMA, and SARIMA models

ARMA, ARIMA, and SARIMA Models Determining the order of p and q Guidelines SARIMA

• Describe how to use control charts for anomaly detection
• Explain Kalman filters
• Use Kalman filters for time series analysis

• Anomaly Detection: Control Charts
• Kalman Filters

• Identify the varieties and usefulness of signal transformations
• Differentiate between various signal transformation techniques

• Sine Wave
• Fourier Transform (FT) and Inverse Fourier Transform (IFT)
• Properties of FT
• Common FT
• Transfer Functions and Bode Plots
• Filters: low pass, high pass, band pass, band stop
• Butterworth Filters
• Window Functions: Hann and Tukey

• Explain how deep learning is used in time series analysis
• Describe RNN and LSTM architectures
• Use Python to implement deep learning models for time series forecasting

• One to One and One to Many Problem
• Recurrent Neural Networks (RNN)
• Long Short Term Memory (LSTM)