Table of Contents

### What is Tableau time series analysis?

Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time. In time series analysis, analysts record data points at consistent intervals over a set period of time rather than just recording the data points intermittently or randomly. However, this type of analysis is not merely the act of collecting data over time.

What sets time series data apart from other data is that the analysis can show how variables change over time. In other words, time is a crucial variable because it shows how the data adjusts over the course of the data points as well as the final results. It provides an additional source of information and a set order of dependencies between the data.

Time series analysis typically requires a large number of data points to ensure consistency and reliability. An extensive data set ensures you have a representative sample size and that analysis can cut through noisy data. It also ensures that any trends or patterns discovered are not outliers and can account for seasonal variance. Additionally, time series data can be used for forecasting—predicting future data based on historical data.

### Why organizations use time series data analysis?

Time series analysis helps organizations understand the underlying causes of trends or systemic patterns over time. Using data visualizations, business users can see seasonal trends and dig deeper into why these trends occur. With modern analytics platforms, these visualizations can go far beyond line graphs.

When organizations analyze data over consistent intervals, they can also use time series forecasting to predict the likelihood of future events. Time series forecasting is part of predictive analytics. It can show likely changes in the data, like seasonality or cyclic behavior, which provides a better understanding of data variables and helps forecast better.

For example, Des Moines Public Schools analyzed five years of student achievement data to identify at-risk students and track progress over time. Today’s technology allows us to collect massive amounts of data every day and it’s easier than ever to gather enough consistent data for comprehensive analysis.

### Time series analysis examples

Time series analysis is used for non-stationary data—things that are constantly fluctuating over time or are affected by time. Industries like finance, retail, and economics frequently use time series analysis because currency and sales are always changing. Stock market analysis is an excellent example of time series analysis in action, especially with automated trading algorithms. Likewise, time series analysis is ideal for forecasting weather changes, helping meteorologists predict everything from tomorrow’s weather report to future years of climate change. Examples of time series analysis in action include:

- Weather data
- Rainfall measurements
- Temperature readings
- Heart rate monitoring (EKG)
- Brain monitoring (EEG)
- Quarterly sales
- Stock prices
- Automated stock trading
- Industry forecasts
- Interest rates

Click here to read more questions and answers

## Questions and Answers:-

Q1. Perform a monthly resample/downsample of the time series. What is the maximum value for February?

Ans:- **17.37**

Q2. Perform a daily resample/upsample of the data. Do interpolation to fill the data. What is the value for Jan 12, 2011?

Ans:- **16.09 **

Q3. Perform a daily resample/upsample of the data. Do a forward filling of the missing values with limit of 2. What is the value for Jan 16,2011?

Ans:- **15.97**

Q4. How many observations have you seen from Jan 1, 2011 to March 31, 2011?

Ans:- **12**

Q5. Perform a monthly resample/downsample of the time series. What is the minimum value for May?

Ans:- **16.26**

Q6. For the XOM stock close prices time series perform a stationarity test using ADF. What is the value of the ADF statistic ?

Ans:- **0.91**

Q7. For the WMT stock open prices time series perform a stationarity test using ADF. What is the p-value?

Ans:- **0.0028**

Q8. For the WMT stock open prices time series perform a stationarity test using ADF. What is the value of ADF statistic?

Ans:- **-3.89**

Q9. For the XOM stock close prices time series perform a stationarity test using ADF. What is the p-value?

Ans:- **0.99**

Q10. For the WMT stock open prices time series perform a stationarity test using ADF. How is the time series behaving?

Ans:- **Stationery**

Q11. Auto Correlation Function Plot can be used for determining if a Time Series is stationary or not.

Ans:- **True**

Q12. Time Series data is indexed by _______________?

Ans:- **Datetime**

Q13. If the p value is > 0.05 during the ADF test of the time series then the series is said to be ___________________.

Ans:- **Stationery**

Q14. I can write my custom aggregation function while resampling my time series in Pandas.

Ans:- **True**

Q15. What package in Python provides features to work with Time Zones?

Ans:- **Pytz**

Q16. Augmented Dickey-Fuller test cannot be used for identifying if a Time Series is Stationary.

Ans:- **False**

Q17. What does freq=’T’ signify while passing this parameter to the date_range() function ?

Ans:- **every minute**

Q18. If the mean and variance of a Time Series is constant over time , it is called a _____________ Time Series.

Ans: **Stationery**

Q19. What is the function to offset the date for daylight saving?

Ans:- **Offset()**

Q20. What is the function to plot a lag plot for a time series using python?

Ans:- **lag_plot()**

Q21. What function in Python helps in creating Date Time index for data that does not have date or time values captured?

Ans:- **date_range()**

Q22. When I upsample my time series and I find many missing values , how do I fill the missing values?

Ans:- **Interpolation**

Q23. Down sampling is the process of converting a ______________ to __________________ frequency.

Ans:- **High, Low**

Q24. What is the function used for plotting the values of a time series using Python?

Ans:- **plot()**

Q25. I cannot plot resampled Time Series data in Python

Ans:- **False**

Q26. In pandas I can combine two time series with different frequencies into a single time series

Ans:- **True**

Q27. In Time Series data , the observations are captured over varying time intervals.

Ans:- **False**

Q28. AIC Stands for

Ans:- **Akaike**

Q29. What is the default aggregation function while resampling a time series in pandas

Ans:- **mean**

Q30. It is a good practice to apply Forecasting models for non-stationary time series

Ans:- **False**