PyData London 2016
This talk will present best-practices and most commonly used methods for dealing with irregular time series. Though we’d all like data to come at regular and reliable intervals, the reality is that most time series data doesn’t come this way. Fortunately, there is a long-standing theoretical framework for knowing what does and doesn’t make sense for corralling this irregular data.
Irregular time series and how to whip them
History of irregular time series
Statisticians have long grappled with what to do in the case of missing data, and missing data in a time series is a special, but very common, case of the general problem of missing data. Luckily, irregular time series offer more information and more promising techniques than simple guesswork and rules of thumb.
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I’ll discuss best-practices for irregular time series, emphasizing in particular early-stage decision making driven by data and the purpose of a particular analysis. I’ll also highlight best-Python practices and state of the art frameworks that correspond to statistical best practices.
In particular I’ll cover the following topics:
Visualizing irregular time series
Drawing inferences from patterns of missing data
Correlation techniques for irregular time series
Causal analysis for irregular time series
Slides available here: https://speakerdeck.com/aileenanielsen/irregular-time-series-and-how-to-whip-them
How to use ML.NET to create predictions, or forecasts, on your time series data.
Code – https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/TimeSeriesForecast/Program.cs
Detect anomalies in time series data in ML.NET – https://www.youtube.com/watch?v=Nu9lz5jNkGs
Detect anomalies with Cognitive Services – https://www.youtube.com/watch?v=gfb63wvjnYQ
ML.NET Playlist – https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx
Gear used (affiliate links):
Mic – https://amzn.to/2YEXtxI
Mouse – https://amzn.to/2ZtASoQ
Introduction of Time Series Forecasting | Part 4 | ARIMA Time Series Forecasting Theory
Hi guys… in this video I have talked about the theory of ARIMA (Auto regressive integrated moving average) time series forecasting methodology. I have tried to explain its component like ACF, PACF and lagged difference with the help of simple example to that you can understand their functioning in ARIMA process.
Theory of Arima time series forecasting methodology
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Time Series Analysis is a major component of a Data Scientist’s job profile and the average salary of an employee who knows Time Series is 18 lakhs per annum in India and 0k in the United States. So, it becomes a necessity for you to master time series analysis, if you want to get that high-profile data scientist job.
Visit Great Learning Academy, for free access to full courses, projects, data sets, codebooks & live sessions: https://glacad.me/3duVMLE
This full course on Time Series Analysis will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India.
He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).
Thus, keeping in mind, the importance of time series analysis, we have come up with this Full-course:
These are the topics covered in this full course:
• Types of statistics – 6:18
• What is Time Series Forecasting? – 21:12
• Components of Time Series – 55:23
• Additive Model and Multiplicative Model in Time Series – 1:16:48
• Measures of Forecast Accuracy – 3:04:55
• Exponential Smoothing – 3:47:50
Time Series Analysis explanation :
Here is a list of our other full course videos:
Probability and Statistics Full Course: https://www.youtube.com/watch?v=z9siRCCElls
Machine Learning with Python: https://www.youtube.com/watch?v=RnFGwxJwx-0&t=287s
Tableau Training for Beginners: https://www.youtube.com/watch?v=6mBtTNggkUk&t=994s
Python for Data Science: https://www.youtube.com/watch?v=edvg4eHi_Mw&t=17669s
Hadoop Full Course: https://www.youtube.com/watch?v=JK2MdJAWEGc
Statistics for Data Science : https://www.youtube.com/watch?v=Vfo5le26IhY&t=6682s
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