Time Series Analysis in Data Science Institutes in Bangalore

Time Series Analysis is a process where you will analyze historical data that has been collected at a specific time frequency (monthly data, weekly data, daily data, and so on).  As an analyst, you will then perform certain key operations in this data like segmentation and forecasting to get the desired results. All the tools for Time Series Analysis are available in the Python library.

Time series process, like other data process methods, may seem to be similar, because it also involves the collection of historical data. However, the key differentiating factor in the time series process is that the data collected over a period of time is recorded with the time when they were collected, and the changes between certain periods are observed carefully, to predict future outcomes. In time series analysis, you will analyze huge volumes of data, how the information has adjusted and evolved over a period of time, understand the pattern or relationships formed there, and then forecast the future data based on the trends.

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How time series analysis is useful for businesses today?

More and more organizations are looking for data analysts with time series analysis skills today, as they have to deal with large volumes of historical data, make sense of the data, establish patterns, understand seasonal variations and make predictions based on them. Here is how time series analysis is ruling the data science department in companies across all verticals today.

Based on the nature of historical data needed for this process, and the techniques used here, time series analysis is widely used in businesses where time-bound data is the key. Some practical examples of time series analysis used today include weather forecasts, stock market price changes, heart rate monitoring, analyzing interest rates for deposits, etc.

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Processes involved in time series analysis

The process of studying the historical data, identifying patterns, and forecasting scenarios based on this past data can be summarized in the following processes.

• Classification – Usually, the first step of the time series analysis, where you analyze the data and put them in various buckets for easy organization
• Curve Fitting – Data in these buckets or categories are plotted along a curve, and this plotting becomes the base for further processes
• Descriptive – Using the plotted curve to understand relationships, patterns, seasonal variations and other correlations within the data
• Explanative – Understanding why these trends and seasonal variations are noticed for specific periods

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• Exploratory – Using data visualization tools to highlight these trends, their reasons and their impact through attractive, visual formats
• Forecasting – Using these past trends and seasonal variations to predict future data, and keeping these scenarios as accurate as possible
• Intervention Analysis – Understanding the impact of a sudden event changing the flow or trend of data
• Segmentation – Splitting the data into various segments based on the properties 

What types of time series analysis models are used today

The models that analysts create after analyzing historical data using time series analysis techniques need to be an exact representation of the sample, and should be as accurate as possible. Sometimes, the data may be too big to stick to one particular model and finalize the trends & reasons for the same. If you are a data analyst, you may be required to use one of these models depending on what you have to forecast, and what type of data you have to deal with:

• Autoregressive Model (AR)
• Moving Average Model (MA)
• Autoregressive Moving Average Model (ARMA)
• Autoregressive Integrated Moving Average Model (ARIMA)
• Seasonal Autoregressive Integrated Moving Average Model (SARIMA)
• Vector Autoregressive Model (VAR)
• Vector Error Correction Model (VECM)

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Learning time series analysis in Bangalore

If you are reading this, you might be already working in the field of data science. Honing an extra skill will only add more value to your career, and make you eligible for a promotion or pay hike. One of the best niche data science skills that you can learn right now is time series analysis. Time is data, these days. So using time-bound historical data to make almost accurate predictions is a field that is never going to go out of vogue.

If you want to learn time series analysis from one of the data science institutes in Bangalore, we recommend you brush up your knowledge on the other fundamental concepts of data science like math, statistics, programming languages like Python & R, querying languages like SQL, coding, algorithms, Artificial Intelligence, Machine Learning, Deep Learning and Natural Language Processing.

Even if you aren’t able to cover all of these topics, you can take up a 4 or 6-month data science course in one of the reputed institutes in Bangalore, 360digiTMG to ensure that you are prepared well to understand the concepts of data science. We suggest 360digiTMG’s data science and data analytics courses because of the following reasons: