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Time series has become one of the most important aspect for data representation and analysis. It is a series of data points in time order which is taken at successive equally spaced points in time. In this tutorial we'll be exploring the concept and application of Time Series in Stock Market.

Forecasting methods like exponential smoothing, Autoregressive Integrated Moving Average (ARIMA) etc. will be discussed in this video so as to give you a good idea about various time series forecasting methods.

A time series is a sequence of observations over a certain period. A univariate time series consists of the values taken by a single variable at periodic time instances over a period, and a multivariate time series consists of the values taken by multiple variables at the same periodic time instances over a period. The analysis of temporal data is capable of giving us useful insights on how a variable changes over time, or how it depends on the change in the values of other variables (s). This relationship of a variable on its previous values and/or other variables can be analyzed for time series forecasting and has numerous applications.

Topics Covered:
Following pointers will be covered in this video:
00:00:00 Introduction
00:01:26 Indices
00:14:36 Simple Exponential Smoothing
00:43:06 ARIMA Approach
01:13:49 Comparing Models

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