Time Series Modeling Fullstack Professional

ENROLLING COHORT 4 NOW!!

This program covers 8 projects
featuring end-to-end methods for time series analysis and forecasting. You will
learn how to go from industry problem/opportunity to discovery techniques, data
requirements modeling, translation of problem statement into a timeseries
experiment, analytics and modeling and model deployment. In addition to
mathematical foundations of time series, you will get hands-on experience
building predictive models in cases of both stationary and non-stationary time
series. You will also gain solid mastery of advanced concepts and the
application of a broad range of tools and techniques used in implementing industry
scale timeseries-based solutions.

Advanced Concept Topics: autocorrelation and partial autocorrelation, Fourier
analysis, stationarity, time series decomposition, autoregressive integrated
moving average (ARIMA) process and the Box-Jenkins methodology, generalized
autoregressive conditional heteroskedasticity (GARCH) model, long short-term
memory (LSTM), a special type of recurrent neural networks (RNN)

Classical Time Series Forecasting Methods:

Autoregression (AR)

Moving Average (MA)

Autoregressive Moving Average (ARMA)

Autoregressive Integrated Moving
Average (ARIMA)

Seasonal Autoregressive Integrated
Moving-Average (SARIMA)

Seasonal Autoregressive Integrated
Moving-Average with Exogenous Regressors (SARIMAX)

Vector Autoregression (VAR)

Vector Autoregression Moving-Average
(VARMA)

Vector Autoregression Moving-Average
with Exogenous Regressors (VARMAX)

Simple Exponential Smoothing (SES)

Holt Winter’s Exponential Smoothing
(HWES)

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