This document provides an overview of a presentation on time series analysis using the QuSandbox platform. The presentation was given by Sri Krishnamurthy, founder and CEO of QuantUniversity, at a QuantUniversity meetup in Boston on November 29, 2018. It covered topics including machine learning techniques for time series analysis, case studies analyzing temperature and swap rate data, and a demonstration of modeling time series data with neural networks.

1. Time Series Analysis with the QuSandbox
2018 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.quantuniversity.com
11/29/2018
QuantUniversity Meetup
Boston

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About us:
• Data Science, Quant Finance and
Model Governance Advisory
• Technologies using MATLAB, Python
and R
• Programs
▫ Analytics Certificate Program
▫ Fintech programs
• Platform

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Our programs
www.analyticscertificate.com

4. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
Professional
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
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• 2-day Advances in Time series Workshop
▫ December/Jan - TBD
▫ Stay tuned for details at www.analyticscertificate.com
• 2-day Machine Learning in Finance Workshop
▫ Jan 23rd & 24th
▫ Register at www.analyticscertificate.com/MachineLearning
Upcoming events

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Slides will be available at:
https://researchhub.qusandbox.com

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You too can do data science!

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• Brief intro to Machine Learning
• Time Series Analysis – Example
• Time Series Analysis – Case study
• Demo
Agenda

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10. Dataset, variable and Observations
Dataset: A rectangular array with Rows as observations and
columns as variables
Variable: A characteristic of members of a population ( Age, State
etc.)
Observation: List of Variable values for a member of the
population

11. Variables
— A variable could be:
—Categorical
— Yes/No flags
— AAA,BB ratings for bonds
—Numerical
— 35 mpg
— $170K salary

12. Datasets
• Longitudinal
▫ Observations are dependent
▫ Temporal-continuity is required
• Cross-sectional
▫ Observations are independent

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Data
Cross
sectional
Numerical Categorical
Longitudinal
Numerical
Summary

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• Given a dataset, build a model that captures the similarities in
different observations and assigns them to different buckets.
• Given a set of variables, predict the value of another variable in a
given data set
▫ Predict Salaries given work experience, education etc.
▫ Predict whether a loan would be approved given fico score, current
loans, employment status etc.
Predictive Analytics : Cross sectional datasets

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• Supervised Algorithms
▫ Given a set of variables !", predict the value of another variable # in a
given data set such that
▫ If y is numeric => Prediction
▫ If y is categorical => Classification
Machine Learning
x1,x2,x3… Model F(X) y

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• Given a time series dataset, build a model that can be used to
forecast values in the future
Predictive Analytics : Time series datasets

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• Goal is to extract and model the various components
Time series datasets

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• https://youtu.be/nIKwXzGieK8?t=63
• Extracting components from time series datasets is like squeezing
juice out of sugarcane.
Sugarcane analogy

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• http://faculty.chicagobooth.edu/ruey.tsay/teaching/introTS/ch3case
2.pdf
Case 1: Analysis of Temperature Anomalies

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Case 2 : Analyzing Swap rates
Ref: Statistical Models and Methods for Financial Markets Tze Leung Lai and Haipeng Xing.
New York, NY: Springer, 2008. ISBN: 978-0-387-77826-6.

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http://www.asimovinstitute.org/neural-network-zoo/

23. Supervised learning
Cross-sectional
▫ Observations are independent
▫ Given X1----Xi, predict Y
▫ CNNs

24. Supervised learning
Sequential
▫ Sequentially ordered
▫ Given O1---OT, predict OT+1
1 Normal
2 Normal
3 Abnormal
4 Normal
5 Abnormal

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• Given : X1,X2,X3----XN
• Convert the Univariate time series dataset to a cross sectional
Dataset
Time series modeling in Keras using MLPs
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
X12
X13
X14
X15
X Y
X1 X2
X2 X3
X3 X4
X4 X5
X5 X6
X6 X7
X7 X8
X8 X9
X9 X10
X10 X11
X11 X12
X12 X13
X13 X14
X14 X15

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• Monthly data
• Computational Intelligence in Forecasting
• Source: http://irafm.osu.cz/cif/main.php?c=Static&page=download
Sample data
0
200
400
600
800
1000
1200
1400
1600
1800
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
103
106

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• Use 72 for training and 36 for testing
• Lookback 1, 10
• Longer the lookback, larger the network
Multi-Layer Perceptron
Size 8
Size 1

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Demo
Train Score: 1972.20 MSE (44.41 RMSE)
Test Score: 3001.77 MSE (54.79 RMSE)
Train Score: 2631.49 MSE (51.30 RMSE)
Test Score: 4166.64 MSE (64.55 RMSE)
Lookback = 1 Lookback = 10

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• Has 3 types of parameters
▫ W – Hidden weights
▫ U – Hidden to Hidden weights
▫ V – Hidden to Label weights
• All W,U,V are shared
Recurrent Neural Networks1
1. http://ai.stanford.edu/~quocle/tutorial2.pdf

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Where can Recurrent Neural Networks be used?1
1. http://karpathy.github.io/2015/05/21/rnn-effectiveness/
1. Vanilla mode of processing without RNN, from fixed-sized input to fixed-sized output (e.g. image
classification).
2. Sequence output (e.g. image captioning takes an image and outputs a sentence of words).
3. Sequence input (e.g. sentiment analysis where a given sentence is classified as expressing positive
or negative sentiment).
4. Sequence input and sequence output (e.g. Machine Translation: an RNN reads a sentence in
English and then outputs a sentence in French).
5. Synced sequence input and output (e.g. video classification where we wish to label each frame of
the video).

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Recurrent Neural Networks
• A recurrent neural network can be thought of as multiple copies of
the same network, each passing a message to a successor. 1
• Backpropagation(computing gradient wrt all parameters of the
network) which is process used to propagate errors and weights
needs to be modified for RNNs due to the existence of loops
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

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• BPTT begins by unfolding a recurrent neural network through time
as shown in the figure.
• Training then proceeds in a manner similar to training a feed-
forward neural network with backpropagation, except that the
training patterns are visited in sequential order.
Back Propagation through time (BPTT)1
1. https://en.wikipedia.org/wiki/Backpropagation_through_time

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• Backpropagation through time (BPTT) for RNNs is difficult due to a
problem known as vanishing/exploding gradient . i.e, the gradient
becomes extremely small or large towards the first and end of the
network.
• This is addressed by LSTM RNNs. Instead of neurons, LSTMs use
memory cells 1
Addressing the problem of Vanishing/Exploding gradient
http://deeplearning.net/tutorial/lstm.html

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www.QuSandbox.com
Model
Analytics
Studio
QuResearchHub
QuSandbox
Prototype, Iterate and tune Standardize workflows
Productionize and share

35. Sri Krishnamurthy, CFA, CAP
Founder and Chief Data Scientist
sri@quantuniversity.com
srikrishnamurthy
www.QuantUniversity.com
www.analyticscertificate.com
www.qusandbox.com
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
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