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Updated in [February 21st, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)


Time series has become one of the most important aspect for data representation and analysis. It is a series of data points in time order which is taken at successive equally spaced points in time. In this tutorial we'll be exploring the concept and application of Time Series in Stock Market.

Forecasting methods like exponential smoothing, Autoregressive Integrated Moving Average (ARIMA) etc. will be discussed in this video so as to give you a good idea about various time series forecasting methods.

A time series is a sequence of observations over a certain period. A univariate time series consists of the values taken by a single variable at periodic time instances over a period, and a multivariate time series consists of the values taken by multiple variables at the same periodic time instances over a period. The analysis of temporal data is capable of giving us useful insights on how a variable changes over time, or how it depends on the change in the values of other variables (s). This relationship of a variable on its previous values and/or other variables can be analyzed for time series forecasting and has numerous applications.


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
This course will provide learners with the skills and knowledge to analyze and forecast time series data in the stock market. Learners will gain an understanding of the concept and application of time series in stock market, as well as an overview of various time series forecasting methods such as exponential smoothing and Autoregressive Integrated Moving Average (ARIMA). Additionally, learners will gain an understanding of the analysis of temporal data and its relationship to time series forecasting, and its various applications.

How does this course contribute to professional growth?
This course contributes to professional growth by providing an in-depth understanding of time series in stock market forecasting. It covers topics such as exponential smoothing, Autoregressive Integrated Moving Average (ARIMA), univariate and multivariate time series, and temporal data analysis. Through this course, professionals can gain a better understanding of how a variable changes over time and how it is affected by other variables. This knowledge can be applied to various forecasting applications, allowing professionals to make more informed decisions and improve their performance.

Is this course suitable for preparing further education?
This course appears to be suitable for preparing further education in the field of Time Series in Stock Market Time Series Forecasting Data Science. It covers topics such as exponential smoothing, Autoregressive Integrated Moving Average (ARIMA), univariate and multivariate time series, and time series forecasting. These topics provide a good foundation for further study in the field.