Week 19 and 20 : 1/11/2018-16/11/2018

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Time Series Forecasting

We had sessions with our mentor Varun Goel from Tatras,Delhi throughout the week and so on different techniques used in forecasting.
We covered following topics in these sessions:
●Forecasting – Introduction
●Linear Regression
●Moving Averages
●Exponential Smoothing
●ARMA, ARIMA

●Neural Networks, BRANNs

What is forecasting?

Forecast: (verb) to calculate or predict (some future event or condition) usually as a result of study and analysis of available pertinent data
Things we forecast…
–Weather parameters
–Sales numbers/ Demand
–Agricultural Production
–Currency exchange rates, Stock prices, Gold prices, Interest rates
–Internet Traffic
–Loss due to Epidemics and Natural Disasters and wars

Forecasting for  a Stationary Time Series

A stationary time series has the form: Dt = m + e t , where m is a constant and e t is a random variable with mean 0 and var σ2 

Methods:

●Naive Method
●Moving Averages
●Weighted Moving Averages

Simple exponential smoothening

          


Holt-Winter's Method - When Trend and seasonality are present















All the concepts and algorithms were then implemented in python.
An example snippet is shown below: 



OUTPUT:



Traffic police interaction

Along with learning time series forecasting,we also had interaction with Punjab traffic police personnel and they shared with us some projects and ideas they would like us to work upon in the future and various points were discussed. We started working on a mobile application also for them.
Location: Sahibzada Ajit Singh Nagar, Punjab, India

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