The first one- Let’s define the time series and its analysis!

Venali Sonone
Dec 2, 2019 · 4 min read

For me, time series is simply

“data which reflects additional information of relevance of time in its structure.”

Let’s try to check four different perspectives of four famous platforms and define time series and it’s analysis.

As I always say. The real impact of data science is measured by its monetary value or business impact.

So let’s see what Investopedia has to say

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What Is a Time Series? A time series is a sequence of numerical data points in successive order. In investing, a time series tracks the movement of the chosen data points, such as a security’s price, over a specified period of time with data points recorded at regular intervals. There is no minimum or maximum amount of time that must be included, allowing the data to be gathered in a way that provides the information being sought by the investor or analyst examining the activity.

Understanding Time Series A time series can be taken on any variable that changes over time. In investing, it is common to use a time series to track the price of a security over time. This can be tracked over the short term, such as the price of a security on the hour over the course of a business day, or the long term, such as the price of a security at close on the last day of every month over the course of five years.

Time Series Analysis Time series analysis can be useful to see how a given asset, security, or economic variable changes over time. It can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period.

Time Series Forecasting Time series forecasting uses information regarding historical values and associated patterns to predict future activity. Most often, this relates to trend analysis, cyclical fluctuation analysis, and issues of seasonality. As with all forecasting methods, success is not guaranteed.


Alternatively, let’s take more general view and define by wiki way

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A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.

Time series analysis comprises methods for analyzing time-series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called “time series analysis”, which focuses on comparing values of a single time series or multiple dependent time series at different points in time. Interrupted time series analysis is the analysis of interventions on a single time series.


Lastly a short one from Quora

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Time Series is a sequence of data points measured at regular time-intervals over a period of time. Irregular data does not form Time-Series.

It uses statistical methods to analyze time-series data and extract meaningful insights about the data.

The data points are collected over a period. These data points (past values) are analyzed to forecast the future. Obviously, It is time-dependent.


Though time-series can be a monster like NLP or any other field in ML this article and following tries to give an overview and educate enough so as to do advanced stuff in the field and also give strong enough work to establish/create solid real-world use-cases in an applied domain.

A beautiful start is Little Book of R for Time Series! link

The best place to find the datasets and linked the best algorithms with accuracies: link



Credits

I would be adding several articles in the index as the series progresses but first following is a gentle start in this series The series has references to several data sources, packages, research papers, blogs, books, vlog, practical advice, industrial work, and personal experiences. I thank everyone in the field and especially whose work I was exposed to be able to put this up together. All credits to the smart folks out there!


Also, this series is the first deliberate effort for establishing self proven ground without expectations of appreciations in a career ladder. While everyone lives amidst of unknown struggle and discovery of purpose I personally think it is okay to sneak a moment of a smile and express gratitude for all right or wrong life choices taken in varied circumstances as you couldn’t have chosen better otherwise.

Data Driven Investor

from confusion to clarity, not insanity

Venali Sonone

Written by

Data Scientist by profession and just lazy by nature.

Data Driven Investor

from confusion to clarity, not insanity

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