time series analytics

Time Series AnalyticsPresented By:

Harsh Narula

Copyright © 2015 Accenture All rights reserved. 2

• Joined Accenture 5 years back (First company YAY!)

• Working on Data Warehousing domain since 2010

• Have expertise on SAP BODS, Microsoft SSIS, SSRS, SQL Server, Informatica, IDQ etc.

• Worked with Clients like Microsoft, Farmers Insurance, Intertek (Current)

• Microsoft certified Business Intelligence Developer

• Love Travelling, Eating, Jazz Music, Cooking, and Bartending (I make good cocktails, yes)

• Aspiring data scientist, working on a model to forecast solar radiation to help qualify future

sites for Solar Power Station Installation (Sounds cool right? It’s NOT! )

About Me


Analytics – Key to Success

“With the rising complexity of global business, gut decisions and hunches no longer suffice. Successful responses to threats and opportunities now depend on rapid and smart execution. Let me state it plainly: Business analytics is the key to achieving these challenging objectives”-Jim Goodnight, CEO, SAS

“Human beings often make a long list of excuses not to be analytical, but there’s plenty of research showing that data, facts, and analysis are powerful aids to decision making, and that the decisions made on them are better than those made through intuition or gut instinct” – Tom Davenport, Jeanne G Harris and Robert Morrison ( Analytics at Work)


Analytics –What is it? What does it do?

• Analytics is the science of analysis. It makes extensive use of data, statistical and

quantitative analysis, explanatory & predictive modeling, and fact-based

management to drive decision making

• Analytics may be used as input for human decisions or may drive fully automated

decisions


Why Business is investing in Analytics?

Competitive Advantage

Gather insights into the target markets

Align customers to the right products

Adjust to the changing behavior and expectation of

new generation customers.

Help you understand customer’s buying patterns

Reduce the cost of doing business

Generate more revenue and profits

Acquire more customers

Helps you to know which customers are risk prone

and what are the ways to retain them.

Helps you to manage risks.

Increase value, execution into business processes.


Business Impact of Actionable Insights

• Strategic Planning

• Consumer Management

• Product Management &

Development

Use Consumer Insights to drive Innovation & improve Customer Experience

Social Media Analytics

Consumer trend analysis

New Product Forecasting

CRM Analytics

Marketing

Sales & Account Planning

FinanceIncrease ROI on Spend

Business Impact Business Function Actionable Insights

Marketing Mix Analytics

Trade Spend Analytics

Variable Cost Estimation

Profit Modeling

Channel Management

Procurement,

Distribution & Logistics

Increase Collaborationw/ Channel & Suppliers

Demand Signal Repository

Returns Management

Partner Performance Analytics

Channel Profitability

• Manufacturing• Logistics & Distribution• Regulatory and Compliance• Energy Management

Achieve Operational Excellence

Traceability Analytics

Production data analytics

Carbon Management

Spare Parts Optimization

• Supply Chain• Manufacturing• Regulatory and Compliance

Optimize End-to-end Costs

Inventory Optimization

Warranty Analytics

SKU rationalization

Plant Efficiency


What is Time Series?


Introduction

• A Time Series is a sequence of data points, typically consisting of successive

measurements made over a time interval

• Time Series analysis comprises methods for analyzing time series data in

order to extract meaningful statistics and other characteristics of the data

Few time series patterns:


Some Trends

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Relevance of Time Series

• Energy and financial markets generates complex

data which need special treatment

• Uncertainty, randomness, seasonality, volatility,

and non-stationarity related to these markets

sometimes make it difficult to take appropriate

business decisions

• Electricity pool markets are said to be more

complex than stock markets

• The proposed course employs advanced data

analytics techniques for modeling and

forecasting of energy and financial markets

• These techniques can also be applied in areas

like marketing, sales etc.


Time Series Forecasting can be done through various

methodologies:

1. Moving Averages

2. Regression

3. Auto Regression Moving Averages (ARIMA)

4. Generalized Auto Regressive Conditional

Heteroscedasticity (GARCH, eGARCH, mGARCH,

sGARCH etc.)

5. Cointegration and Vector Auto Regression

Time Series Techniques


• The n-period moving average builds a forecast by averaging the observations in the most recent n periods:

where xt represents the observation made in period t, and At denotes the moving average calculated after making the observation in period t.

What is Moving Average?

At = (xt + xt–1 + … + xt–n+1) / n

13

A statistician can have his head in an oven and his feet

in ice, and he will say that on the average he feels fine.



methodologies:

1. Moving Averages

2. Regression




sGARCH etc.)




• Regression analysis is concerned with the study of the relationship

between one variable called explained or dependent variable (y) and

one or more other variables called independent or explanatory

variables (x1, x2……xn)

Y = f (x1, x2……xn)

What is Regression?


How Regression is Done?

Theory: y = f(x1, x2 …..x)

Mathematical model of theory; y = B1 + B2 x2 + B3 x3 … + Bn xn (Deterministic)

Data collection on y, x2 , x3 …. (time series, cross-sectional, panel

Estimation of sample b1, b2 ….bn through OLS (min RSS)

Hypothesis testing (t- stat Ho : B2 = 0, F- stat Ho : B2 = B3 =… Bn = 0 )

Diagnostic Checking (errors free from M-C, H-C, A-C & e ~ N (0, σ2)

Use estimated model for information extractions and/or forecasting

Explanatory Power of the Model (R2 / Adj R2 )

17

Problems with Linear

Regression:

1. Only looks at Linear Relationships

2. Only looks at the mean of the

Dependent Variable

4. It is Sensitive to Outliers

5. Data must be Independent



methodologies:

1. Moving Averages

2. Regression




sGARCH etc.)




Why ARIMA, GARCH, and VAR

• Traditional estimation

methods look only

towards the past

values, average it, and

forecast it.

• However, they do not

account for volatility,

trends, and

seasonality etc.

• These models can

take into account all

these factors, and

produce accurate

results.


Forecasting With Time Series Models

• Two important features:

• Uses historical data for the phenomenon we wish to forecast

• We seek a routine calculation to apply to a large number of cases and that may be automated, without relying on qualitative information about the underlying phenomena

• Short-term forecasts are often used in situations that involve forecasting many different variables at frequent intervals

• We separate the base component, forecast the base, and apply seasonality and trends to the forecasted series

Base Series

Trends

Seasonality


How We Forecast


How We Forecast

Time

Co

nsu

mption o

f C

oke

• Remove Trends

• Remove Seasonality

• Achieve White Noise

• Forecast Stationary

Series

• Apply Trends and

Seasonality back


Summary

Time

Series

Is

Pure

Science


Questions

time series analytics

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