Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.
Time Series Analysis has become an especially important field in recent years.
With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
COVID–19 has shown us how forecasting is an essential tool for driving public health decisions.
Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.
Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.
We will cover techniques such as:
ETS and Exponential Smoothing
Holt’s Linear Trend 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, Support Vector Machines, and Random Forests)
Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
GRUs and LSTMs for Time Series Forecasting
We will cover applications such as:
Time series forecasting of sales data
Specification: Time Series Analysis, Forecasting, and Machine Learning
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