Time Series Forecasting Based on PyAF and fbProphet

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Year : August 7, 2023 | Volume : 01 | Issue : 01 | Page : 32-36

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Shailesh Yadav, Vikas Yadav

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    1. Research Scholar, Research Scholar,MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR),Maharashtra, Maharashtra,India, India

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    Abstract

    n Time series forecasting is the technique of predicting future events using previous data. Time series data includes information that is collected and recorded at regular intervals, such as daily stock prices, monthly sales figures, or hourly temperature readings. The purpose of time series forecasting is to use previous data to create accurate forecasts about the future values of a given variable. This can be beneficial for a range of applications, including financial forecasting, weather forecasting, and energy demand forecasting. Time series forecasting is an important tool in many fields, including business, economics, finance, and engineering. It can help one make informed judgements about things like production planning, inventory management, and investment strategies. It is also employed in a variety of domains, including meteorology, epidemiology, and ecology. Its applications are far-reaching and contribute to making well-informed decisions in areas like production planning, inventory management, and investment strategies. Moreover, this technique finds extensive use in diverse fields like meteorology, epidemiology, and ecology, where it aids in predicting weather patterns, analyzing disease trends, and understanding ecological systems. By utilizing time series forecasting, professionals in different domains can harness the power of historical data to gain insights and make informed decisions.n

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    Keywords: Structured, big data, sortable, data processing, unstructured, semi-structured.

    n [if 424 equals=”Regular Issue”][This article belongs to International Journal of Information Security Engineering(ijise)]n

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    How to cite this article:n Shailesh Yadav, Vikas Yadav Time Series Forecasting Based on PyAF and fbProphet ijise August 7, 2023; 01:32-36

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    How to cite this URL: Shailesh Yadav, Vikas Yadav Time Series Forecasting Based on PyAF and fbProphet ijise August 7, 2023n {cited August 7, 2023};01:32-36. Available from: https://journals.stmjournals.com/ijise/article=August 7, 2023/view=0/

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    1. Taylor SJ, Letham B. Business Time Series Forecasting at Scale. PeerJ Preprints 5: E3190v2, 35 (8), 48–90.
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    Volume 01
    Issue 01
    Received May 12, 2023
    Accepted July 4, 2023
    Published August 7, 2023

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