Henri Parviainen

November 3, 2021

21 min read

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Time series data is omnipresent in our lives as we can encounter it in pretty much any domain. Sensors, monitoring, weather forecasts, stock prices, exchange rates, application performance metrics are just a few examples.

In this article we will summarize all the knowledge that you may need in order to use time series data as a way to gain business insights or conduct a study. We will learn the main concepts related to gathering, organizing and usage, including types and formats, ways to store and collect it, as well as diving into the fundamentals of analysis and visualization techniques.

Let’s begin by clearly defining what it actually is, as well as what it isn’t. Basically, time series data is any type of information presented as an ordered sequence.

It can be defined as a collection of observations for a single subject assembled over different, generally equally spaced, time intervals. Or, to put it simply, time series is data (observations or behavior) collected at different points in time and organized chronologically.

Time is the central attribute that distinguishes time series from other types of data. The time intervals applied to assemble the collected data in a chronological order are called the time series frequency. So having time as one of the main axes would be the main indicator that a given dataset is a time series.

Now let’s clarify what kinds of data are not time series to get this out of the way. Even though it is very common to use time as an axis in datasets, not all collected data is time series. Data can be divided into several types based on time or, in other words, how and when it was recorded.

Here are three basic types of data, divided by the role of time in a dataset presentation:

- Time series data, as we learnt, is collected over a specified continuous period of time.
- Cross-sectional data serves as an opposite concept, as this type relies on collecting and organizing various kinds of data at a single point in time.
- Pooled data is a term used when a combination of time series and cross-functional data is used.

Since time series are used for multiple purposes and in various fields, the datasets can vary. Here are the three main characteristics that would allow you to easily identify such datasets:

- Data arrives in time order
- Time is a primary axis
- Newly collected data is recorded as a new entry

When it comes to the different types of time series data, which is something we are going to talk about in more detail later (as there are so many different types and categories that need to be mentioned), it can be divided into two main categories based on the approach to measuring it.

**Flow time series data** refers to focusing on measuring the activity of attributes over a given time period, which is typically an interval in the total time axis.

**Stock time series data**, on the other hand, is measuring attributes at a static point in time.

It wouldn’t be an overstatement to say that data—and our ability to collect and organize data with previously unseen speed and efficiency—is a key component of technological development. Being able to utilize gathered data allows us to build better systems, improve the efficiency of virtually all processes and predict future events in real time.

In today’s world, time series models are truly ubiquitous. Time-axed datasets are widely used across various industries, from finance and economics to sciences, healthcare, weather forecasting and engineering.

As data rapidly becomes one of the most valuable commodities in today’s business world, the importance of time series data is increasing as well. This is why it is so important to understand what it is, and how using data in the right way can allow an organization to improve and optimize virtually all work processes.

Since time is fundamental and the most integral part of our life, it plays a role in gathering any observable information. In other words, it won’t be wrong to say that you can find it everywhere. And it gets increasingly ubiquitous today, as the Fourth Industrial Revolution unfolds and organizations across the globe face the need to implement more and more IT systems, sensors and tools, all of which are able to produce and/or collect data.

Being able to analyze and access insightful data is quickly becoming a necessity for almost any organization, including businesses, social and educational institutions. Time series data is a valuable commodity, and its value in today’s world appreciates mightily.

Being so omnipresent, time series obviously has a myriad of different applications across industries. In order for you to understand how it is used in practice, here are several examples.

**Industrial companies**

Digitalization of manufacturing, production plants and related industries produces an increasing amount of time series data. This data comes from an increasing amount of sensors which is being used. Examples of sensors and data includes pressure, levels, temperature, pH and velocity.******Financial industry**

The absolute majority of financial applications today collect, store and analyze time series data for multiple purposes, such as security, support, improvement of services, and income maximization. Needless to say, banks and financial institutions of various kinds rely on time series data heavily today. As another financial example, stock market traders use algorithms based on real time data to optimize performance.**Consumer electronics**

Modern-day consumer electronics is an industry with a rapidly-growing appetite for data, mainly because electronic devices and systems that we are using become more and more complex. Smart home systems, for example, have access to multiple kinds of time series data, which they analyse in order to identify humans and animals, adjust temperature, detect intruders, etc. Autonomous vehicles and robotic devices of various kinds also use data as an essential component that allows them to move in space and avoid collisions.**Healthcare**

Time series data plays a vital role in healthcare as well, and the recent COVID-19 epidemic is a great illustration of this, as the collection of accurate and timely data allows the global community to stay informed about the ongoing trends in COVID-19 statistics. In this case, time series data helps to save lives by allowing healthcare and government institutions to quickly react upon receiving the new information. When it comes to more specific healthcare-related procedures, heart rate monitoring (ECG) and brain monitoring (EEG) are two crucial procedures that rely on time series data.**Retail industry.**

Time series data is collected in various forms by the retail industry, allowing enterprises to monitor their supply and delivery chains with previously unseen efficiency. The data about moving of various goods enables retail enterprises to optimize stock and provide consumers with discounts or services such as same-day delivery.

Time series data can be divided into a number of types based on different criteria. Knowing the differences between the types of data can be very important as it affects the way you can interact with it and how the database can store and compress this data. Let’s go through some of the most common ways to categorize it, with examples and illustrations.

One characteristic of time series data that is important to understand is based on the relationship to the regularity of measurements collected in the series. Depending on whether the data is collected at regular or irregular time intervals, it can be classified as metrics or events.

**Metrics** is a type of measurement gathered at regular time intervals.

**Events** is a type of measurement are gathered at irregular time intervals.

Regular data that is evenly distributed across time and thus can be modeled or used for processes such as forecasting is called metrics. You need to have metrics in order to use modeling, forecasting and producing aggregates of all kinds.

Here are several examples of metrics.

**Heart rate monitoring (ECG) and brain monitoring (EEG)**

Heart rate monitoring, also known as electrocardiogram (ECG), brain monitoring (electroencephalogram, EEG) and other similar health-related monitoring methods are examples of metrics as they are measuring the activity of human organs at regular time periods.

**Weather conditions**

All kinds of weather conditions monitoring, such as daily temperature, wind, air pressure, heights of ocean tides, etc., is another example of metrics that are used to build data models, which allow us to predict weather changes that will occur in the future.

**Stock price changes**

Measurements of stock price fluctuations are also gathered at regular time intervals.

Events measurements are gathered at irregular time intervals. Because they are unpredictable, intervals between events are inconsistent, which means that this data can’t be used for forecasting and modeling as this would lead to unreliable future projections.

In simpler words, events are a type of data that is focused on capturing the required data whenever it is generated, which could happen with varying time intervals or as a burst.

Here are several examples of events.

**Bank account deposits/withdrawals **

Financial monitoring of bank accounts and ATMs is one example of events time series when the data gathered is the deposits and/or withdrawals, which typically occur at unpredictable time periods.

**Computer logging**

In computing systems, log files serve as a tool that records either various kinds of events that occur in an operating system and other software, or messages between different users of a communication software. Since all of this happens at irregular time intervals, the information in logs can be classified as events.

Another way to classify it, is by the functional forms of data modeling, which divides it into **linear** and **nonlinear**.

In linear time series, each data point can be viewed as a linear combination of past or future values. In other words, a linear time series is generated by a linear equation and can be modeled as a linear AR (auto-regressive) model.

One way to specify if the time series is linear is by looking at how X and Y change in accordance to each other. When X increases by 1 and Y increases by a constant rate accordingly, then the data is linear.

Here’s an example of a linear graph.

Nonlinear time series, on the other hand, are generated by nonlinear dynamic equations and have features that can’t be modelled by linear processes, such as breaks, thresholds, asymmetric cycles and changing variances of different kinds. This is why generating and processing nonlinear time series data is typically a lot more difficult and requires complex modeling processes.

Here’s one example of a nonlinear data:

Moving further, let’s take a look at several key aspects of time series data that are important for you to understand in order to be able to work with and use it.

One approach that is fundamental to time series data, and applied across all computing systems in general, is immutability. Which is quite a simple concept to understand.

Immutability is the idea that data (objects in functional programming languages) should not be changed after a new data record was created. Instead, all new records should only be added to the existing data.

As new information in time series data comes in time order, it is recorded as a new entry. And in line with the immutability principle, entries in time series data are usually not being changed after they were recorded and are simply added to all the previous records in a database.

So now it should be fairly easy to understand why immutability is a core concept to time series data. Such a database is always (or ‘typically,’ to be more precise) treated as a time-ordered series of immutable objects. Having immutability incorporated allows to keep time series databases consistent and unchanging as all queries are performed at a particular point-in-time.

Immutability as a core principle is something that differentiates time series data from relational data. As opposed to time series, the relational data, which is stored in relational databases, is typically mutable as it is used for online transaction processing and other requirements where new events take place on a generally random basis. Although these events still occur in chronological order (events of any kind just can’t exist outside of time), time is not relevant to such data and therefore is not used as an axis. For time series data, on the other hand, time changes are always essential.

Now let’s talk in more detail about the three basic types of data that we mentioned previously, and how time series data is different from cross-sectional, pooled and panel data. Understanding these concepts is another key element to being able to utilize time series data with multiple benefits.

As we learnt, time series data is a collection of observations for a single subject assembled over different time intervals. Having time as an axis is a distinctive feature of time series data.

Now let’s compare it to cross-sectional and pooled/panel data.

Cross-sectional data serves as an opposite concept, as this type relies on collecting and organizing various kinds of data at a single point in time. Cross-sectional data doesn’t rely on natural ordering of the observations so the data can be entered in any order.

Data on max temperature, wind or humidity at any day in a year can be a good example of cross-sectional data. As well as closing prices of stocks on a stock market, or sales of a store inventory at any given day.

Pooled (or cross-sectional time series data) and panel (also called longitudinal) data are two rather close concepts, both used to describe data where time series and cross-sectional measurements are combined.

Essentially, both panel and pooled data rely on combining cross-sectional and time series measurements. The only difference is in the relationship to the units (entities), around which the data is collected (e.g. companies, cities, industries, etc.).

**Panel data** refers to multi-dimensional data that uses samples of the same cross-sectional units observed at multiple points in time.

**Pooled data** uses random samples of cross-functional units in different time periods, so each sample of cross-sectional data taken can be populated by different units.

Undoubtedly, these data type explanations can still leave some people confused. Here’s an easy way to differentiate these data types from each other.

- Time series are measurements on a single unit/entity over time.
- Cross-section are measurements on different units at a single point in time.
- If the data is presented both in multiple units and over a time period, it is panel or pooled data.

Time series data is typically stored in time series databases (TSDBs) that are specifically built or optimized for working with timestamp data, be it metrics or events. Since time series data is frequently monitored and collected in huge volumes, it needs a database that can handle massive amounts of data.

TSDBs are also required to be able to support functions that are crucial to the efficient utilization of time series records, such as data summarization, data lifecycle management, and time-related queries.

Here is a list of features and capabilities that a purpose-built or optimized database should have in order to handle time series data work well:

- Support timestamps at different scales of precision (e.g. seconds, milliseconds, microseconds, or nanoseconds);
- Compression of variable levels in order to write high volumes of data;
- High throughout ingest and real-time querying;
- Retention policies for automatically removing the expired old time series;
- Time series data import capabilities and the ability to interact with SQL-like query languages;
- Built-in analytics, aggregation, filtering, interpolation, and smoothing functions for easy identification of trends or anomalies.

- Prometheus
- Apache Druid
- Apache Pinot
- InfluxDB
- IBM Informix
- MongoDB
- OpenTSDB
- AWS Timestream
- TimescaleDB
- Graphite

Now, after we learnt what time series data is and how it is stored, the next logical step would be to talk about the actual utilization of time series data. And this is where time series analysis comes into play.

Time series analysis refers to a specific way of analyzing a time series dataset—or simply a sequence of data points gathered over a period of time—to extract insights, meaningful statistics and other characteristics of this data.

Naturally, time series analysis requires time series data, which has a natural temporal ordering and has to be recorded at consistent time intervals. This is something that differentiates time series analysis from cross-sectional data studies where data is tied to one specific point in time and can be entered in any order.

You should also not confuse time series analysis with forecasting, which is a type of time series analysis, as it basically uses historical TS data to make predictions using the same approach.

Time series data analysis relies on identifying and observing patterns that serve as a way to extract actual information from a dataset using one of the available models.

Here are some of the most common patterns observed in time series data.

**Trend**

A trend exists when there is a continuous and long enough change in data, be it upward or downward direction. Trends don’t have to be linear and sometimes can be used to detect a change of direction. For example, increasing trend or decreasing trend would signal about a negative or positive change in sale of some item month-over-month.**Seasonality**

A seasonal pattern is used when a time series data is affected by seasonal factors, which include all nature and weather-related influencers, such as season of the year, day of the month, time of day, etc. One feature of the seasonality pattern is that it always has pre-known and fixed frequency.**Cycle (cyclic behaviour)**

As the opposite to seasonal pattern, cycle patterns occur when the data is affected by factors that are not of fixed and known frequency. This pattern is often applied in economy-related studies, where cyclic behaviour is observed in accordance to cycles in economy and business environment. The average length of cycles is usually longer than the length of seasonal patterns. The duration of cycles in time series data analytics typically is two years or more with a higher variable applied as well.**Anomaly/outlier**

The detection of anomalies and outliers in time series datasets is essentially the opposite side of patterns, as the analysis is focused on identifying all kinds of unusual changes, desired or not, in the dataset.**White noise**

Finally, in some cases the data doesn’t follow any particular patterns with all the data units being in a seemingly random order. This kind of time series data pattern, or the absence of thereof to be precise, is called white noise.

Detecting these and other patterns by applying various models to time series databases is how you can use time series analysis to achieve different goals.

Here are some of the most common types of time series analysis that can be utilized depending on the end-goal of your study.

**Forecasting**

As we learnt earlier, forecasting uses historical time series data to make predictions. Historical data is used as a model for the same data in the future, predicting various kinds of scenarios with future plot points.

**Descriptive analysis**

Descriptive analysis is the main method used to identify the above-described patterns in time series data (trends, cycles, and seasonality).

**Explanative analysis**

Explanative analysis models attempt to understand data, relationships between data points, what caused them and what was the effect.

**Interrupted (intervention) analysis**

Interrupted (intervention) time series analysis, also known as quasi-experimental analysis, is used to detect changes in a long-term time series from before to after a specific interruption (any external influence or a set of influences) took place, potentially affecting the underlying variable. Or, in simpler words, it studies how a time series data can be changed by a curtain event.

**Regression analysis**

Regression analysis is most commonly used as a way to test relationships between one or more different time series. As opposed to regression analysis, regular time series analysis refers to testing relationships between different points in a single time series.

**Exploratory analysis**

Exploratory analysis highlights main features identified in time series data, typically in visual format.

**Association analysis**

Association analysis is used to identify associations between any two features in a time series dataset.

**Classification**

Classification is used to identify and assign properties to time series data.

**Segmentation**

Segmentation analysis is applied to split the time series data into segments based on assigned properties.

**Curve fitting**

Curve fitting is used to study the relationships of different variables within a dataset by organizing them along a curve.

Now let’s talk a little bit about models that are commonly used for time series data. There is a huge variety of models, representing different forms and stochastic processes. Not to get into too much detail, in this article we will only describe a few of the most popular and common models.

When looking at the process level, three broad classes of linear time series models should be specified.

Here they are:

**Autoregressive (AR) models**

AR model is a representation of a type of random process, which is why it is used for data describing time-varying processes, such as changes in weather, economics, etc.**Integrated (I) models**

Integrated models are series with random walk components. They are called integrated because these series are the sums of weakly-stationary components.**Moving-average (MA) models**Moving-average models are used for modeling univariate time series. In MA models, the output variable depends linearly on the current and various past values of an imperfectly predictable (stochastic) term.

These three classes in various combinations produce the following three commonly used in time series data analytics models.**Autoregressive moving average (ARMA) models**

ARMA models combine AR and MA classes, where AR part involves regressing the variable on its own past values, while MA part is used to model the error term as a linear combination of error terms occurring contemporaneously and at various times in the past. ARMA models are frequently used for analytics and predicting future values in a series.**Autoregressive integrated moving average (ARIMA) models**

ARIMA models are a generalization of an ARMA model and are used in cases where data show evidence of non-stationarity qualities, where an initial differencing step, corresponding to the integrated part of the model, can be applied one or more times to eliminate the non-stationarity of the mean function.

Both ARMA and ARIMA models are frequently used for analytics and predicting future values in a series.**Autoregressive fractionally integrated moving average (ARFIMA) models**

ARFIMA model, in its turn, generalizes ARIMA models (or, basically, all the three basic classes) by allowing non-integer values of the differencing parameter. ARFIMA models are frequently used for modeling so-called long memory time series where deviations from the long-run mean decay slower than an exponential decay.

When it comes to nonlinear time series models, there are a number of models that represent changes in variability over time that are predicted by or related to recent past values of the observed series.**Autoregressive conditional heteroscedasticity (ARCH) models**

ARCH is one such model, which describes the variance of the current error term or innovation as a function of the actual sizes of error terms.in the previous time periods.

In order to perform time series analysis, you would normally require a fairly large number of data points as a way to ensure reliability and consistency.

One problem that can limit your ability to utilize time series data for analytics, forecasting and other purposes is the absence of high-quality extensive datasets. This issue is easily solvable, however, as you can find a fair amount of time series datasets that include enormous volumes of data, publicly available online.

Here are several great sources where you can find large numbers of highly variable datasets.

- GitHub

GitHub is able to provide you with one of the widest and most diverse selections of time series databases on the internet. Here is one great list of topic-centric public data sources in high quality that can be found on GitHub. They are collected and tidied from blogs, answers, and user responses. Most of the datasets listed are free, but not all of them. Awesome time series database is one more great curated list of time series databases that are available for filtering based on language, backend, etc. - Federal Reserve Economic Data (FRED)

Federal Reserve Economic Data is another major source of publicly available datasets that you should check out if you need good quality time series data. FRED’s library is totally free and contains hundreds of thousands of data series points collected from over 70 sources on various topics across countries and industries. - Google’s dataset search engine

Chances are, you didn’t know that the search giant Google has a dataset search service designed for the specific purpose of helping users to find publicly available data. It allows you to search by data formats, update dates, topics, types of licenses and so on. The datasets you will find by using Google’s search will be smaller in size in most cases, but still useful for creating time series models. - UEA & UCR Time Series Classification Repository

UEA & UCR Time Series Classification Repository is an ongoing project aiming to develop a comprehensive repository for research into time series classification. Currently TSC archive has 128 huge datasets that are all free and publicly available. - Kaggle

Kaggle is the world's largest data science community that has a total of over 50,000 public datasets and 400,000 public notebooks available. Kaggle also has its own dataset search engine with thousands of publicly available time series datasets being indexed. - CompEngine

CompEngine is a self-organizing database of time-series data, which allows you to upload time-series data and interactively visualize similar data that have been measured by others. With CompEngine, you can not only find great time series datasets, but also use a specialized time series data comparison engine, which allows users to upload their own data and visualize how it relates to time series data measured and generated by others. In total, CompEngine’s repository has almost 30,000 time series datasets with over 140 mln data points. - Data portals

Data portals is a comprehensive list of open data portals from around the world. Currently, it has 591 data portals that mostly belong to government and public institutions of various kinds.

Despite having all these wonderful sources of time series data around the world, depending on your specific needs, you may still face a challenge of not having enough data. In such a case, the only solution would be to create your own time series dataset.

Now, creating and using time series datasets is a very broad topic that would require an extensive guide or tutorial on its own to cover all the aspects of this work. We can say, however, that collecting time series data, generating datasets and utilizing them for various purposes gets easier day by day thanks to a number of great libraries and frameworks that are designed to make it easier to work with time series data.

Let’s take a look at several great Python libraries and packages that you can use to create time series datasets, as well as using them afterwards to build models, generate predictions, etc.

- Timeseries Generator

A repository of python packages to generate synthetic datasets in a generic way from Nike open-source. Generate data based on many factors such as random noise, linear trend, seasonality or a combination of these factors. The package also provide a web-based UI to generate datasets in an interactive web UI. - Pandas

Pandas is another Python library created for various data manipulation and analysis purposes. Pandas provides you with tools for reading and writing data between in-memory data structures and different file formats, supports data alignment, integrated handling of missing data, reshaping and pivoting of data sets. It also allows various other kinds of time series data manipulation operations such as merging, selecting, as well as data cleaning, and data wrangling. - TSFRESH

TSFRESH or Time Series Feature is a Python package that contains many feature extraction methods and a robust feature selection algorithm. TSFRESH is able to automatically extract hundreds of features from a time series. Those features describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic. These sets of features can then be used as datasets to construct statistical or machine learning models on the time series. - TimeSynth

TimeSynth is an open source Python library for generating synthetic time series for model testing. The library can generate regular and irregular time series. The architecture enables users to match different signals with different architectures, allowing a vast array of signals to be generated. - PyFlux

PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array of inference options that can be applied to these models. By combining breadth of models with breadth of inference, PyFlux allows for a probabilistic approach to time series modelling. - Cesium

Cesium is an open source library that allows users to extract features from raw time series data, build machine learning models from these features, and generate predictions for new data.

Another crucial element of utilizing time series data is visualizations. In order to extract valuable information and insights, your data has to be presented as temporal visualization to showcase the changes at different points in time.

Time series data is typically visualized using specialized tools that provide users with multiple visualization types and formats to choose from. Let’s take a look at some of the most common data visualization options.

Graph is a visual representation of data in an organized manner. Time series graphs, also called time series charts or time series plots, are probably the most common data visualization instrument used to illustrate data points at a temporal scale where each point corresponds to both time and the unit of measurement.

Even though these two terms are frequently used interchangeably, they are not exactly the same thing. Charts is the term for all kinds of representations of datasets in order to make the information clear and more understandable. Graphs, being mathematical diagrams showing the relationship between the data units over a period of time, are commonly used as a type of charts in time series data visualization.

Real time graphs, also known as data streaming charts, are used to display time series data in real time. This means that a real time graph will automatically update after every several seconds or when the new data point is received from the server.

Here are some of the most common types of time series visualizations.

Line graph is probably the most simple way to illustrate time series data. It uses points connected to visualize the changes. Being the independent variable, time in line graphs is always presented as the horizontal axis.

Histogram charts visualize the data by grouping it into bins, where bins are displayed as segmented columns. All bins in a histogram have equal width, while their height is proportional to the number of data points in the bin.

Dot plots or dot graphs present data points vertically with dot-like markers, with the height of each marker group representing the frequency of the elements in each interval.

Scatter plots or charts use the same dot-like markers that are scattered across the chart area of the plot, representing each data point. Scatter plots are usually used as a way to visualize random variables.

Trend line is based on standard line and plot graphs, adding a straight line that has to connect at least two points on a chart, extending forward into the future to identify areas of support and resistance.

Time series data is translated into charts, graphs, and other kinds of consumable analytical information that provides organizations with often invaluable insights, using data visualization tools, which can come in the form of software or SaaS solutions.

Data visualization tools allow organizations to create dashboards with easy to understand visualizations of key trends and KPIs. It is also increasingly common for modern data visualization solutions to provide users with a simple drag-and-drop interface to allow users with no technical and/or coding skills to work with time series data and create dashboards with visually-presented data. These tools are typically designed specifically to visualize already organized data and are not intended to be used for time series data analysis. Some of them, however, may include additional features, allowing users to perform multiple types of activities, such as exploring, organizing and interacting with data, as well as collaborating and sharing data between members of the same organization.

- When choosing a data visualization tool for use, check if it meets all the main general requirements for such a solution:

- Supports data import from a wide range of sources, including database querying, application connectors, and file uploads.

- Has user-friendly and simple to use visual no-code interface for interactions with data.

- Provides visual presentation of all KPIs in real time.

- Allows multiple users to work on the same datasets and visualizations at the same time.

- Enables easy export of all your data to multiple channels, including directly to Excel and via API.

- Offers multiple kinds of visualizations for time series data metrics.

Here’s a list of the best tools for your time series data visualization based on the requirements listed above.

- Clarify

Clarify is a powerful time series intelligence tool designed primarily for industrial teams. Clarify combines a number of features, allowing users to integrate, organize, collaborate and visualize industrial data. It supports a streaming data timeline technology that allows users to quickly navigate and visualize hundreds of data signals at the same time without losing overview or performance.

This tool has extensive collaboration capabilities, which is one of its main distinctive features. It allows team members to tag each other to start a thread or log incidents, directly in the data timeline. Adding media files for more context, search and review of previous activities to avoid solving the same issue twice are some of other convenient features of Clarify. Developer-friendly documentation and secure APIs make it easy and secure to work with this solution as well. - Microsoft Power BI

Power BI (a part of Microsoft Power Platform) is a business analytics service from Microsoft that provides users with interactive visualizations and business intelligence capabilities. It has a simple visual interface allowing users with no technical knowledge to create their own reports and build dashboards. Power BI allows you to easily connect to, model, and visualize data, and has additional features such as end-to-end data protection, a big library of data connectors, and simplified data sharing across Microsoft Office applications such as Microsoft Teams and Excel. - Grafana

Grafana is an open source multiplatform analytics and monitoring solution that supports a number of interactive visualization features. Grafana provides users with charts, graphs, and alerts for the web when connected to supported data sources.

Unified approach to data that allows users to import it from various sources, including Kubernetes cluster, raspberry pi, different cloud services, and Google Sheets, is another notable feature of Grafana. It allows users to easily create dynamic dashboards, work on time series data in collaboration with other people and share visualized data with team members. - Seeq

Seeq is a specialized application for process data analytics. It allows users to search their data, add context, cleanse, model, find patterns, establish boundaries, monitor assets, collaborate in real time, and interact with time series data in other ways. One of Seeq’s distinctive features is the support of easy data integration from multiple platforms and databases, including legacy solutions. In particular, Seeq supports integration with Honeywell PHD, GE Proficy, as well as the PI System and relational data from SQL Server, Oracle, and MySQL. Users can access and combine different datasets from multiple sources without the need to write code. - Azure Time Series Insights

Azure Time Series Insights is a fully managed analytics, storage, and visualization service that is designed to make it easier to explore and analyze data related to IoT events. It provides users with a global view of their data for quick validation of IoT solutions to avoid costly downtime of mission-critical devices.

Azure Time Series Insights is able to store, visualize, and query large amounts of time series data in general, whether they are related to IoT events or not. This solution allows you to collect streams of data from multiple IoT devices, easily integrate it into the platform, store and access it. Azure Time Series Insights also includes rich graphic visualizations and tools to conduct deep analysis into IoT data. Open APIs allow users to connect time series data to machine learning solutions or other visualization systems.