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Correcting Non - Stationary Time Series Data With Python

The video begins with a review of previous discussions on determining if a given time series is stationary. It mentions using tests like the Augmented Dickey-Fuller (ADF) test. The focus then shifts to methods for making a non-stationary time series stationary through various transformations.

The video explores methods to make a non-stationary time series into a stationary one, primarily focusing on financial data analysis. It starts by reviewing the use of tests such as the Augmented Dickey-Fuller (ADF) test to check stationarity. The presenter then discusses various transformations to achieve stationarity:

Deflation: Using the Consumer Price Index (CPI) as a reference, the presenter explains deflating data to account for inflation, which helps in normalizing the time series.
Log Transformation: The application of the natural logarithm to data is suggested as a method to reduce non-linear trends to linear, simplifying the analysis.
Differencing: The video discusses using differences between consecutive observations to remove trends and cycles, focusing on the calculation of changes rather than absolute values.

These methods are intended to make the data more suitable for analysis, recognizing that not all techniques will work universally and their effectiveness depends on the specific dataset.

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0:00 Introduction
0:25 Ways to convert Non Stationary Data to Stationary
1:39 How To Get Gold Data From Nasdaq Website with API Key
4:34 How To Apply Deflation on Inflation Data in Consumer Price Index
5:30 How To Apply Log on Non Stationary Data
6:57 How To Test Autocorrelation
7:36 How To Apply Differencing in Non Stationary Data


How To Test For Stationary In Data    • Unlocking Time Series:Master Stationarity ...  

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