Summary

==========

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

Time series modelling has its application s 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 mat h 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 s olve 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

===============

Learn the essential concepts and state of the art. Learn to do time s
eries modelling. Get exposed to some of the tools and software.

Aud
ience

==========

This is suitable for students\, data science prof
essionals and data science enthusiasts. Prior knowledge of Python programm
ing is required. Otherwise\, no prior knowledge of time series forecasting
is required.

Preparation

============

1. Download and inst
all Python 3: https://www.python.org/downloads/

2. Install NumPy: pip install
numpy

3. Install Pandas: pip install pandas

4. Read up on descript
ive statistics (mean/variance/correlation)

5. Read up on hypotheses te
sting

Content

=========

Time series concepts introduced wit
h use cases

Time series concepts and math - Toy dataset - Excel

St
ationarity of time series\, test for stationarity

ACF/PACF and intuiti
ve p/q/d

Modelling time series with AR/MA/ARMA/ARIMA/GARCH

Unit ro
ot and its significance

Timeseries models to RNN - intuition

Tr
ainer

========

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