Download Time Series Analysis, Forecasting, and Machine Learning

What you would learn in Time Series Analysis, Forecasting, and Machine Learning course?

Hello, and welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.

Time Series Analysis has become an increasingly significant field in the last few years.

• With the inflation rate rising, Many are looking to the market for stocks and cryptocurrency to ensure that their savings don't go away.

• COVID-19 has proven that forecasting is essential to inform public health choices.

• Companies are getting more efficient in their planning of the needs for inventory and operations in advance.

Let's cut right to the short. This isn't your standard Time Series Analysis course. The course covers cutting-edge technologies like deep learning and classification of time series (which can provide insights for users from data collected by smartphones or help you understand your thoughts through electrical activity within the brain) and much more.

We will discuss methods like:

• Extensive Smoothing

• "Holt's" Linear Trend Model

• Holt-Winters Model

• ARIMA, SARIMA, SARIMAX and Auto ARIMA

• ACF and PACF

• Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)

• Machine Learning Models (including Logistic Regression and Support Vector Machines as well as Random Forests)

• Deep Learning Models (Artificial Neural Networks, Convolutional Neural Neural Networks as well as Recurrent Neural Networks)

• GRUs and LSTMs to aid in Time Series Forecasting

We will be covering applications like:

• Forecasting sales time series data

• Time-series forecasting of stock prices as well as return on the stock

• The classification and time-series of data from smartphones to identify user behavior

This version will provide more fascinating subjects, including:

• AWS Forecast (Amazon's modern forecasting API that uses low-code API)

• The GARCH (financial model of volatility)

• FB Prophet (Facebook's time series library)

Course Content:

• Extensive Smoothing and ETS Models
• Holt's Linear Trend Model and Holt-Winters
• Moving Average and Autoregressive Models (ARIMA)
• Seasonal ARIMA (SARIMA) and SARIMAX
• Auto ARIMA
• The statsmodels Python library
• PMdarima Python library. pmdarima Python library
• Machine learning in time series forecasting
• Deep learning (ANNs, CNNs, RNNs as well as LSTMs) used to predict time series
• Tensorflow 2 to predict the return on investment and stock prices
• Vector autoregression (VAR) and vector moving average (VMA) models (VARMA)
• AWS Forecast (Amazon's time series forecasting service)
• FB Prophet (Facebook's time series library)
• Forecasting and modeling financial time series
• Garch (volatility modeling)

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