Time series analysis allows Data Scientists to recognize trends, seasonality, and correlations within past data related to an organization to make predictions on which business decisions are based. Let’s take a look at how various industries use Time series analysis to make crucial decisions. ~ The airline industry can optimize travel routes by predicting future weather patterns, seasonal demands, or unexpected events. ~ Stockbrokers use it to predict correlations within stocks & market conditions to decide where to invest. ~ Supply Chain companies can predict weather conditions, traffic patterns, expected delivery times to optimize routes. But did you know that you can build Time Series models with minimum knowledge of coding? The KNIME Analytics Platform can make this happen. It uses a Graphical User Interface to allow Data Scientists who are just starting out and do not have extensive experience in coding. In this webinar, our Machine Learning expert will help you build time series models using the KNIME Analytics Platform. Business leaders and Data scientists must not miss this opportunity to arrive at smart, data-driven business decisions with the help of this platform.

1. Time Series Analysis with
KNIME
Presented By: Shubham Goyal
Data Scientist
Knoldus Inc. & MachineX Intelligence

3. 3
Our Agenda
01 Importance of supply chain management
02 Introduction to time series analysis
03 Components of time series (Autocorrelation, Seasonality,
Stationarity)
04 Modeling time series
05 Knoldus Forecasting platform

4. 4
About Knoldus MachineX
MachineX is a group of data wizards.
We are a team of Data Scientist and engineers with a product
mindset who deliver competitive business advantage.

5. 3 Pillars of Knoldus

6. Our Global Presence
8+ Years
Years of Profitable Growth
155+ People
Largest Scala + Spark + Tensorflow +
Pytorch Services Company
04 Offices
Offices globally
17+ Customers
Multi-year Global Customers

7. Our Partners
Through our strategic partnerships, we have an unwavering commitment to equip your organization
with the knowledge, skills, expertise, resources and tools to succeed.

8. Knoldus MachineX Offerings
Natural Language Processing
Computer Vision Solutions
Data mining
Chatbot Development
Artificial intelligence research and solutions

9. 9
An Intelligent
Meeting Assistant
Application
Record Videos
View DashBoard

10. 10

11. 11
Machine learning
library in scala
KSAI
FishEye

12. Case Studies
Enabling Intelligent systems for enterprises

13. Challenges:
● Forecasting earthquakes is one of the most important problems in Earth
science because of their devastating consequences.
● Predicting the time remaining before laboratory earthquakes occur
from real-time seismic data.
● The data provided was in segments and was messy in nature.
● The signal had a certain time-trend that caused some issues specifically
on mean and quantile based features
Solution:
● Sampled 10 full earthquakes multiple times (up to 10k times) on Data, and
comparing the average KS statistic of all selected features
● Used Matplotlib to visualize the data in every step to extract features.
● We have used LightGBM , Neural networks and XGB Regressor for
prediction modeling.
Results:
● The overall mean square error score on this was 1.83
● The solution was awarded by silver medal by kaggle.
● The Solution was in top 2% overall world ranking.

14. Challenge:
● Various products of storage systems with various configuration leads to
loss track on the health and maintainability
● Customer were unable to take advantage of Software updates due to
lack of easy access to information about compatibility
● Failure handling was not precise due to lack of information on the usage
trend
Solution:
Made a common portal for the customers where they can take
advantage of different predictive features for all kind of HPE storage
systems.
Result:
● All kinds of system status are available in the common portal for all of
their storage systems which helps in taking various maintenance actions
● Software recommendation helps the users keeping updated their
system’s os and different softwares and avoid different anomalies
● Prediction on when the storage might go out of space, when cpu
utilization might go at peak or to summarize prediction on when a
disaster will happen, helps the users avoid various loses.

15. 15
Enable organizations to
capture new value
and business capabilities
Innovation Labs
Consistently blogging, to
share our knowledge,
research
Blogs
Deeplearning, Coursera,
Stanford certified
professionals
Certifications
Insight & perspective to help
you to make right business
decisions
TOK Sessions
It’s great to contribute back
to the community. We
continuously advance open
source technologies to meet
demanding business
requirements.
Open Source
Contribution

16. Machine Learning and AI in
Retail

19. Supply chain management

21. 21
Eliminating
overstocks and
out-of-stocks
FishEye
AI helps retailers replenish supplies by identifying demand
for a particular product based on
● sales history
● location
● weather
● promotions
● trends
● … and so on.

23. Customer Sentiment analysis

24. TIME SERIES ANALYSIS

25. Definition
A time series is a set of observation taken at specified times, usually at
equal intervals”. “A time series may be defined as a collection of reading
belonging to different time periods of some economic or composite
variables
● Time series establish relation between “cause” & “Effects”.
● One variable is “Time” which is independent variable & and the
second is “Data” which is the dependent variable.

26. Examples

27. Importance
of Time
Series
Analysis
● Safety from future
● Utility Studies
● Sales Forecasting
● Budgetary Analysis
● Stock Market Analysis
● Stock Market Analysis
● Process and Quality Control
● Inventory Studies
● Economic Forecasting
● Risk Analysis & Evaluation of
changes.

28. TIME SERIES ANALYSIS

29. Components
of Time
Series
Is it stationary?
Is there a seasonality?
Is the target variable
autocorrelated?

30. Autocorrelation

31. Seasonality

32. Stationarity

34. Modelling time series
ModelsARIMA
Model
Discovery
ARCH/
GARCH
Model
LSTM
Autoregres
sive or VAR
model

35. Knoldus forecasting platform

36. Steps
STEP A
Data Preprocessing
STEP B
Data Visualization/
Analysis
STEP C
Data Inspection
STEP D
Forecasting results
and models
A B C D
Process Steps
Information
Knoldus Forecasting platform will take a dataset in any form and load it in its database, and give different
option to user for data filtration and preprocessing. In step B, it will give you an Analytics dashboard for
reading different aspects from data. After that in Step C , It will give data inspection plot for seasonality, trend
and stationarity of data. In final Step D, It will give you all forecasting result and trained models

37. Data preprocessing

38. Data Visualization/ Analysis

39. Data Inspection

40. Conclusion

43. Feedbacks!!!!
hello@knoldus.com

44. 44
Thank You
www.knoldus.com
+(91) 1204287693
hello@knoldus.com
@Knolspeak
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