Time Series Forecasting (Free)

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Details

Summary
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What do stock prices, interest rates, sales figures and product quality metrics have in common? They are all sequential data. Data is dynamic and might look like it's varying unpredictably. But careful analysis might bring out trends and patterns. Special statistical and modelling techniques have been invented to analyze this sort of data. Given past data, we can predict, within some limits of accuracy, what the data is going to be in future. This is what time series forecasting is all about.

Time series modelling has its applications in the fields of economics, science and social studies. The math behind time series models is complex but a good sense intuition, comprehension and application can simplify the topic. This workshop aims at limiting math to what is required for application while simplifying concepts to solve time series problems. At the end of this workshop, you would be able to solve time series problems with confidence and not be limited by black box models.

Cost
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Workshop is free but do make an optional donation towards Devopedia Foundation. We suggest Rs. 200. Please pay by cash. We'll give you a receipt.

Skills Imparted
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Learn the essential concepts and state of the art. Learn to do time series modelling. Get exposed to some of the tools and software.

Audience
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This is suitable for students, data science professionals and data science enthusiasts. Prior knowledge of Python programming is required. Otherwise, no prior knowledge of time series forecasting is required.

Preparation
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1. Download and install Python 3: https://www.python.org/downloads/
2. Install NumPy: pip install numpy
3. Install Pandas: pip install pandas
4. Read up on descriptive statistics (mean/variance/correlation)
5. Read up on hypotheses testing

Content
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Time series concepts introduced with use cases
Time series concepts and math - Toy dataset - Excel
Stationarity of time series, test for stationarity
ACF/PACF and intuitive p/q/d
Modelling time series with AR/MA/ARMA/ARIMA/GARCH
Unit root and its significance
Timeseries models to RNN - intuition

Trainer
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Ramanathan RM. Data scientist by profession. Strong fundamentals in statistics. Expert in SAS, R and Python.