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How does a commercial bank forecast the expected performance of their loan portfolio?
Or how does an investment manager estimate a stock portfolio’s risk?
Which are the quantitative methods used to predict real-estate properties?
If there is some time dependency, then you know it – the answer is: time series analysis.
This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.
In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:
· Easy to understand
· Comprehensive
· Practical
· To the point
· Packed with plenty of exercises and resources
But we know that may not be enough.
We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…
Welcome to Time Series Analysis in Python!
The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.
We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.
Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.
With these tools we will master the most widely used models out there:
· AR (autoregressive model)
· MA (moving-average model)
· ARMA (autoregressive-moving-average model)
· ARIMA (autoregressive integrated moving average model)
· ARIMAX (autoregressive integrated moving average model with exogenous variables)
. SARIA (seasonal autoregressive moving average model)
. SARIMA (seasonal autoregressive integrated moving average model)
. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
· ARCH (autoregressive conditional heteroscedasticity model)
· GARCH (generalized autoregressive conditional heteroscedasticity model)
. VARMA (vector autoregressive moving average model)
We know that time series is one of those topics that always leaves some doubts.
Until now.
This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.
What you get?
· Active Q&A support
· Supplementary materials – notebook files, course notes, quiz questions, exercises
· All the knowledge to get a job with time series analysis
· A community of data science enthusiasts
· A certificate of completion
· Access to future updates
· Solve real-life business cases that will get you the job
We are happy to offer a 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and start mastering time series in Python today.
Setting Up the Environment
Introduction to Time Series in Python
Creating a Time Series Object in Python
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11Introduction to Time-Series Data
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12Introduction to Time Series Data
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13Notation for Time Series Data
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14Notation for Time Series Data
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15Peculiarities of Time Series Data
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16Peculiarities of Time Series Data
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17Loading the Data
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18Loading the Data
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19Examining the Data
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20Examining the Data
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21Plotting the Data
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22Plotting the Data
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23The QQ Plot
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24The QQ Plot
Working with Time Series in Python
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25Transforming String inputs into DateTime Values
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26Transforming String inputs into DateTime Values
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27Using Date as an Index
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28Using Dates as an Index
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29Setting the Frequency
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30Setting the Frequency
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31Filling Missing Values
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32Filling Missing Values
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33Adding and Removing Columns in a Data Frame
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34Adding and Removing Columns in a Data Frame
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35Splitting Up the Data
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36Splitting Up the Data
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37Appendix: Updating the Dataset
Picking the Correct Model
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38White Noise
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39White Noise
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40Random Walk
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41Random Walk
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42Stationarity
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43Stationarity
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44Determining Weak Form Stationarity
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45Determining Weak Form Stationarity
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46Seasonality
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47Seasonality
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48Correlation Between Past and Present Values
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49Correlation Between Past and Present Values
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50The Autocorrelation Function (ACF)
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51The Autocorrelation Function (ACF)
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52The Partial Autocorrelation Function (PACF)
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53The Partial Autocorrelation Function (PACF)
Modeling Autoregression: The AR Model
Adjusting to Shocks: The MA Model
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56The Autoregressive (AR) Model
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57The Autoregressive (AR) Model
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58Examining the ACF and PACF of Prices
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59Examining the ACF and PACF of Prices
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60Fitting an AR(1) Model for Index Prices
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61Fitting an AR(1) Model for Index Prices
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62Fitting Higher-Lag AR Models for Prices
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63Fitting Higher-Lag AR Models for Prices
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64Using Returns Instead of Prices
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65Using Returns Instead of Prices
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66Examining the ACF and PACF of Returns
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67Examining the ACF and PACF of Returns
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68Fitting an AR(1) Model for Index Returns
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69Fitting an AR(1) Model for Index Returns
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70Fitting Higher-Lag AR Models for Returns
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71Fitting Higher-Lag AR Models for Returns
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72Normalizing Values
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73Normalizing Values
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74Model Selection for Normalized Returns (AR)
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75Model Selection for Normalized Returns
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76Examining the AR Model Residuals
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77Examining the AR Model Residuals
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78Unexpected Shocks from Past Periods
Past Values and Past Errors: The ARMA Model
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79The Moving Average (MA) Model
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80The Moving Average (MA) Model
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81Fitting an MA(1) Model for Returns
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82Fitting an MA(1) Model for Returns
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83Fitting Higher-Lag MA Models for Returns
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84Fitting Higher-Lag MA Models for Returns
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85Examining the MA Model Residuals for Returns
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86Examining the MA Model Residuals for Returns
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87Model Selection for Normalized Returns (MA)
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88Model Selection for Normalized Returns (MA)
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89Fitting an MA(1) Model for Prices
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90Fitting an MA(1) Model for Prices
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91Past Values and Past Errors