## What Is Time Series Analysis in Machine Learning?

When thinking about machine learning, how often do you consider time? Time series analysis is an important area of machine learning  because many predictive problems that ML is used to solve involve a critical time component.

Often, solving these predictive problems is sometimes neglected because the time component makes these problems more difficult to handle and process. Time series analysis is the method by which such problems can be effectively solved.

Below, we’ll discuss what time series analysis is and some practical, real-life applications.

What is time series analysis?

A time series is a series of data points indexed in time order. The daily values of the Dow Jones Industrial Average and a car’s engine temperature measurements taken from a sensor are both examples of a time series. Time series analysis includes methods for analyzing these series of data to extract meaningful statistics, predict future values, or detect anomalies.

Time series analysis comes in several forms, including:

• Descriptive analysis: determining trends and patterns, such as “cyclic patterns, turning points, and outliers”.
• Spectral analysis: separating and determining periodic or cyclical components in a time series.
• Forecasting: predictions on the future based on historical trends.
• Intervention analysis: determining if a specific event has lead to a positive or negative change in the time series.
• Explanative analysis: determining the relationship between two or more time series analyses.

A variety of different techniques are used to carry out time series forecasting, including:

• Multilayer perceptron.
• Bayesian neural networks.
• Generalized regression neural networks (also called kernel regression).
• K-nearest neighbor regression.
• CART regression trees.
• Gaussian processes.

What are the benefits?

In a nutshell, time series analysis is most often instrumentalized for its predictive function, since it is capable of authoritatively predicting future trends and changes. This predictive power can be leveraged by a variety of practical applications. Stock price prediction, equipment malfunction detection, and cyber attack monitoring are a few key examples of how time series analysis can be applied to real-life scenarios. Other applications include:

• Meteorology: weather variables, like temperature, pressure, wind.
• Economy and finance: economic factors (GNP), financial indexes, exchange rate, spread.
• Marketing: activity of business, sales.
• Industry: electric load, power consumption, voltage, sensors
• Biomedicine: physiological signals (EEG), heart-rate, patient temperature.
• Web: clicks, logs.
• Genomics: time series of gene expression during cell cycle.

The value of a time series framework?

A typical time series workflow usually requires multiple iterations, trying different models and algorithms, tweaking their parameters and applying them in many different combinations. As a result, it is a manual, time-consuming, and complex process.

At Digamma.ai, our team has made this development process better, faster and more efficient by developing our own proprietary time series analysis framework.

The framework consists of four parts including data preprocessing, exploratory analysis, anomaly detection and forecasting. Each of these four methods includes a variety of models and algorithms each customizable with options and parameters. In short, our framework completely automates the traditional time series workflow.

Our approach allows for the use of a variety of effective methods quickly, iteratively and easily to refine a time series model instead of having to rely on onerous trial and error experiments.

Importantly, our framework is scalable to very large datasets and can be deployed to the cloud, including Amazon AWS and other providers.

## Digamma.ai AI Q&A Series: Jackie Snow, MIT Tech Review

Digamma.ai AI Q&A Series: Jackie Snow, MIT Tech Review

1. We are in the very early stages of AI in history. Style is still so complex and there is this sense that AI is trending towards an intelligent assistant that will help us look like we shop at Saks Fifth Avenue regularly and yet do it under budget. In what areas do you believe AI will help consumers and what areas of the style and retail experience do you believe humans still need to do themselves?

I would agree that style is still too complex for AI to get a grip on. So, there’s a long way to go before AI is putting together an outfit for me. Right now, with everything that we’re seeing in the fashion world, I think it’s really geared towards predicting what sort of items a consumer might like to buy. That doesn’t really have anything to do with style. In the meantime, I do think we’re going to have AI that can help surface a lot of different items that we may not necessarily be exposed to through the online shopping environments that are available to us right now. Read More

## Digamma.ai Partners With the Institute of Mathematics at the National Academy of Sciences of Ukraine

Our team here at Digamma.ai is very excited to announce our partnership with the Institute of Mathematics of the National Academy of Sciences of Ukraine in Kyiv.

Through the Kyiv Academic University’s Dual Education Program, we will working with Masters and PhD students from the Department of Mathematics to tackle key machine learning projects together.

With mathematics and statistics as the foundational underpinnings of algorithms that are powering the emerging AI economy, the mathematics field — and researchers in the sector — are experiencing a flood of interest from the private sector.

Through this initiative, we will have an opportunity to work with some of the brightest mathematical minds at the University — which has a long-standing history of exceptional achievements in the field —  and provide an environment for mathematics graduate students to apply their research and knowledge to solving real-life problems using machine learning methodologies. Read More

## Digamma.ai CEO Q&A: Amar Chokhawala, CEO of Reflektion

Digamma.ai CEO Q&A Series: Amar Chokhawala, CEO of Reflektion

Reflektion seeks to give retailers a deeper understanding of customer intent. How does your company do this from a technology perspective?

In today’s world, everyone is sharing everything. So, the customer’s intent almost starts with temporal signals. When I’m looking for a place to eat in the morning, that means I’m looking mostly at restaurants which are actually serving a breakfast or lunch. Here’s an example of how the temporal aspect comes into play. Imagine a three year old—if you asked her what an “apple” is, she would say it was a fruit. If you posed that same question to a thirteen year old, she would say that it’s the technology company. So, in order to understand customer intent in retail, we need to build a comprehensive user profile that takes into account this temporal dimension. Because if you don’t know the consumer, then you won’t know what the intent of that particular consumer is. Our technology is capable of building those user profiles. We focus on the user first and, to date, have built 600 million plus profiles. That allows us to gain a deeper understanding of customer intent. We use machine learning because, unlike other technology, it works more effectively with more data. The more data you have, the better the model performs. Read More

## Why Companies Need To Tackle Big Data With Machine Learning

By 2018, 50,000 gigabytes of data will be created per second. A significant amount of that data will be stored in corporate server farms. A report from IDG found that “[m]anaging unstructured data is growing as a challenge – rising from 31 percent in 2015 to 45 percent in 2016.” IDC’s Digital Universe report found that “the amount of data stored in the world’s IT systems” doubles every two years. On one hand, many companies are eager to start confronting the challenge of big data. But according to an IDG Enterprise report, 90% of the companies surveyed reported running into major problems when implementing or developing their big data initiatives. No wonder an October 2016 report from Gartner found that most companies who attempted to implement a big data project were mostly stuck in the pilot stage. The challenge of what do to with big data is daunting for many companies. However, machine learning has an important role to play in “solving” big data. Read More

## Digamma.ai CEO Q&A Series: Oliver Tan, CEO of ViSenze

Digamma.ai CEO Q&A Series: Oliver Tan, CEO of ViSenze

1. How are you looking to fundamentally change the online shopping experience for both consumers and retailers through ViSenze?

Visenze’s mission is simple. We want to simplify the way people discover the visual world with a clear philosophy: “Search not by keyword but by image.” By this, we mean revolutionizing search not by processing standard metadata, but by processing pixels, images, frames and videos and extracting all of this intelligence to help shoppers find things that they want. Pixels represent a trove of intelligence. We want to transform how image queries are conducted, both online as well as in the real world.

2. Using machine learning and computer vision technology, ViSenze can recommend visually similar items to online shoppers. Other companies claim to do this as well — what are the key advantages of ViSenze’s technology?

The space in which we operate, namely visual search and image recognition have been around for a while. It’s not a new space. Every company focuses on a couple areas in order to be better and sharper than their competitors. Similarly, for ViSenze, we’re not a general visual search company and, in every vertical that we focus on, we get down to the granularity and specificity of the domain itself.

We get down very deeply in understanding different visual attributes. For instance, in the fashion space, you need to understand the distinction between a mandarin collar and a standard collar. You also need to understand different types of sleeves: is it a half-shoulder sleeve? A cap sleeve? A quarter sleeve? We get down to that level of specificity in the very same way that a shopper would walk into a store and look for a specific dress knowing what she wants.

If we can at least equal that experience online by aggregating all these search attributes into an intelligent method of recommendations, we would have been very helpful to the online shopper.

## Digamma.ai CEO Q&A Series: Jonas Cleveland, CEO of COSY

Digamma.ai CEO Q&A Series: Jonas Cleveland, CEO of COSY

What transformative effects do you intend COSY to have on the retail sector?
We describe COSY as an aisle intelligence company that’s using machine vision and AI to improve retail execution and inventory productivity for our customers in retail stores and warehouses. The major trend occurring in retail today is the evolution of the retail store floor into a distribution center.  This is something that COSY has been talking about for some time. For consumers, this means being able to order online, show up to the store and grab what you ordered plus other things, such as fresh produce.

For retailers, this means that they need more stores so that they can reach more people efficiently. Today we see stores like Target moving into more urban environments. We also see many stores closing down as the overhead of these stores is too high.

There is also the issue of an inefficient use of space. So, really, what COSY enables is the ability to optimize the way this real estate is used. Essentially, being able to optimize how you place departments, products and organize them on the store floor to drive revenue higher for retailers. Read More

## Digamma.ai Hosts Panel on AI Infrastructure: Google, Uber AI Labs, Pluto AI and MedNition

Our latest event, AI’s Potential: From Policy to Posterity, featured an experienced panel of AI and machine learning specialists. Our panelists included:

•    Douglas Bemis, CTO and Co-Founder at Uber AI Labs

•    Gus Katsiapis, Senior Staff Software Engineer at Google

•    Prateek Joshi, Founder at Pluto AI

•    Christian Reilly, Co-Founder of MedNition

The event featured wide-ranging discussions on the current state of artificial intelligence infrastructure and where we need it to be in the future in order to truly realize its potential.

Our team of machine learning consultants at Digamma believes that we are on the brink of a new AI era in which emerging AI technologies will quickly evolve, mature and require the creation of a new, sophisticated technological infrastructure. The AI we have now is emergent and not fully production ready—it is primarily “custom craft”, not the “AI factory line” that we need to truly scale AI technologies. In light of this, we asked our panelists to provide their perspective on the role of hardware and processing power in enabling the growth and evolution of AI technologies. Read More

## Digamma.ai CEO Q&A Series: Kieran Snyder of Textio

Digamma.ai CEO Q&A Series: Interview with Kieran Snyder, co-founder of Textio

1. You previously worked at Microsoft and Amazon and have a PhD in Linguistics and Cognitive Science. Today, you and your team run Textio, an augmented writing platform that has been used by companies such as Atlassian, NVIDIA, Square, Starbucks, Twitter and Vodafone. How did you and your co-founder and CTO Jensen Harris come up with the idea for Textio back in 2014?

We started Textio with a very simple vision, which is that if every time you wrote something you knew exactly who was going to respond. Now imagine you had the data to tell you why someone would respond. If you know who will respond, and why, you can change your approach to get a different result.

Jensen and I founded Textio together, though we come from very different backgrounds. The union of those backgrounds makes Textio what it is today.

My background is in measurement in of language. I was a Linguistics and Math major in college and my Ph.D. is in natural language processing. I spent around a decade leading natural language and search at Microsoft and Amazon. Jensen’s background is in user experience. He designed the first ever UI for e-mail in the form of Outlook. He led the design and implementation of Word, PowerPoint, Excel kind of core Office UI. Where my experience has always been in measuring the impact of language in software, his has been building enterprise software that’s usable by a billion people. Read More

## Digamma.ai CEO Q&A Series: Wout Brusselaers of Deep 6 AI

Digamma.ai CEO Q&A Series: Interview with Wout Brusselaers, CEO of Deep 6 AI

1. Deep 6 AI recently won the Enterprise and Smart Data category at SXSW’s accelerator competition and participated in the inaugural SXSW Connect to End Cancer event. What role is Deep 6 taking in the fight to end cancer?

We’re not doing any cancer research ourselves. However, Joe Biden, our former Vice President, actually pitched the purpose of our company himself when he was on stage at SXSW. He mentioned that in order for the mutual effort to cure cancer to have a fighting chance, we need to increase the number of patients with cancer that participate in clinical trials. Today, that stands at 4%—only 4% of cancer patients actually enroll in clinical trials. To be able to find a cure for cancer it is widely estimated that that number needs to grow to at least 25% and ideally 50%. Most patients don’t know about clinical trials.

Finding the appropriate clinical trial they are eligible for requires an in-depth, professional understanding of their specific condition, sometimes down to the molecular level. This requires a lot of research, expertise and time. We cannot expect patients to carry this burden to figure out which trial to enroll in, from possibly hundreds available to them, and reach out to all the pharmaceutical companies, the CROs, or the hospitals involved. But trial sponsors or sites also don’t have the tools to look into patient data and quickly assess whether a patient will be a suitable candidate for a given trial. So, that is where artificial intelligence can really help and perform that matching, reducing the time it takes from months to as little as 10 seconds.

After our SXSW presentation, a woman who had Stage 3 breast cancer came up to me and explained how she was looking for a trial to help fight her cancer. Her husband was an oncologist, she was highly educated, and she knew her way around medical data, but she was still overwhelmed trying to find a trial. She said that when she keyed in her basic characteristics she found 140 trials online. She then had to narrow it down by reading through them and reaching out to the different PIs and sponsors. One sent her to a website where she had to take a survey, then a phone interview, and then weeks later they told her that she was not eligible. And she doesn’t even know why. It’s a nightmare. We want to help change that. Read More