Time Series Analysis, Forecasting, and Machine Learning
- Descripción
- Currículum
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Hello friends!
Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.
Time Series Analysis has become an especially important field in recent years.
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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.
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COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
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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:
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ETS and Exponential Smoothing
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Holt’s Linear Trend Model
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Holt-Winters Model
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ARIMA, SARIMA, SARIMAX, and Auto ARIMA
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ACF and PACF
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Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
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Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
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Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
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GRUs and LSTMs for Time Series Forecasting
We will cover applications such as:
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Time series forecasting of sales data
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Time series forecasting of stock prices and stock returns
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Time series classification of smartphone data to predict user behavior
The VIP version of the course will cover even more exciting topics, such as:
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AWS Forecast (Amazon’s state-of-the-art low-code forecasting API)
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GARCH (financial volatility modeling)
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FB Prophet (Facebook’s time series library)
So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.
Thanks for reading, and I’ll see you in class!
UNIQUE FEATURES
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Every line of code explained in detail – email me any time if you disagree
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No wasted time “typing” on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
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Not afraid of university-level math – get important details about algorithms that other courses leave out
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7Time Series Basics Section IntroductionVideo lesson
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8What is a Time Series?Video lesson
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9Modeling vs. PredictingVideo lesson
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10Why Do We Care About Shapes?Video lesson
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11Types of TasksVideo lesson
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12Power, Log, and Box-Cox TransformationsVideo lesson
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13Power, Log, and Box-Cox Transformations in CodeVideo lesson
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14Forecasting MetricsVideo lesson
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15Financial Time Series PrimerVideo lesson
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16Price Simulations in CodeVideo lesson
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17Random Walks and the Random Walk HypothesisVideo lesson
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18The Naive Forecast and the Importance of BaselinesVideo lesson
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19Naive Forecast and Forecasting Metrics in CodeVideo lesson
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20Time Series Basics Section SummaryVideo lesson
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21Suggestion BoxVideo lesson
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22Exponential Smoothing Section IntroductionVideo lesson
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23Exponential Smoothing Intuition for BeginnersVideo lesson
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24SMA TheoryVideo lesson
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25SMA CodeVideo lesson
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26EWMA TheoryVideo lesson
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27EWMA CodeVideo lesson
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28SES TheoryVideo lesson
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29SES CodeVideo lesson
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30Holt's Linear Trend Model (Theory)Video lesson
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31Holt's Linear Trend Model (Code)Video lesson
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32Holt-Winters (Theory)Video lesson
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33Holt-Winters (Code)Video lesson
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34Walk-Forward ValidationVideo lesson
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35Walk-Forward Validation in CodeVideo lesson
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36Application: Sales DataVideo lesson
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37Application: Stock PredictionsVideo lesson
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38SMA Application: COVID-19 CountingVideo lesson
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39SMA Application: Algorithmic TradingVideo lesson
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40Exponential Smoothing Section SummaryVideo lesson
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41(Optional) More About State-Space ModelsVideo lesson
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42ARIMA Section IntroductionVideo lesson
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43Autoregressive Models - AR(p)Video lesson
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44Moving Average Models - MA(q)Video lesson
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45ARIMAVideo lesson
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46ARIMA in CodeVideo lesson
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47StationarityVideo lesson
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48Stationarity in CodeVideo lesson
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49ACF (Autocorrelation Function)Video lesson
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50PACF (Partial Autocorrelation Funtion)Video lesson
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51ACF and PACF in Code (pt 1)Video lesson
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52ACF and PACF in Code (pt 2)Video lesson
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53Auto ARIMA and SARIMAXVideo lesson
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54Model Selection, AIC and BICVideo lesson
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55Auto ARIMA in CodeVideo lesson
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56Auto ARIMA in Code (Stocks)Video lesson
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57ACF and PACF for Stock ReturnsVideo lesson
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58Auto ARIMA in Code (Sales Data)Video lesson
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59How to Forecast with ARIMAVideo lesson
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60Forecasting Out-Of-SampleVideo lesson
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61ARIMA Section SummaryVideo lesson
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62Vector Autoregression Section IntroductionVideo lesson
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63VAR and VARMA TheoryVideo lesson
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64VARMA Code (pt 1)Video lesson
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65VARMA Code (pt 2)Video lesson
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66VARMA Code (pt 3)Video lesson
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67VARMA Econometrics Code (pt 1)Video lesson
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68VARMA Econometrics Code (pt 2)Video lesson
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69Granger CausalityVideo lesson
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70Granger Causality CodeVideo lesson
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71Converting Between Models (Optional)Video lesson
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72Vector Autoregression Section SummaryVideo lesson
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73Machine Learning Section IntroductionVideo lesson
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74Supervised Machine Learning: Classification and RegressionVideo lesson
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75Autoregressive Machine Learning ModelsVideo lesson
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76Machine Learning Algorithms: Linear RegressionVideo lesson
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77Machine Learning Algorithms: Logistic RegressionVideo lesson
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78Machine Learning Algorithms: Support Vector MachinesVideo lesson
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79Machine Learning Algorithms: Random ForestVideo lesson
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80Extrapolation and Stock PricesVideo lesson
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81Machine Learning for Time Series Forecasting in Code (pt 1)Video lesson
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82Forecasting with DifferencingVideo lesson
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83Machine Learning for Time Series Forecasting in Code (pt 2)Video lesson
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84Application: Sales DataVideo lesson
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85Application: Predicting Stock Prices and ReturnsVideo lesson
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86Application: Predicting Stock MovementsVideo lesson
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87Machine Learning Section SummaryVideo lesson