{"id":41340,"date":"2025-10-14T14:05:50","date_gmt":"2025-10-14T14:05:50","guid":{"rendered":"https:\/\/certifeka-edu.com\/programs\/research-for-strategic-development-professional-certificate-2\/lessons\/lesson-4-quantitative-forecasting-methods-time-series-forecasting-2\/"},"modified":"2025-10-14T14:05:50","modified_gmt":"2025-10-14T14:05:50","slug":"lesson-4-quantitative-forecasting-methods-time-series-forecasting-2","status":"publish","type":"lesson","link":"https:\/\/certifeka-edu.com\/ar\/programs\/sample-course\/lessons\/lesson-4-quantitative-forecasting-methods-time-series-forecasting-2\/","title":{"rendered":"Lesson 4: Quantitative forecasting methods – Time series forecasting"},"content":{"rendered":"

\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

Lesson 4: Forecasting and Predictive Analytics
\n<\/h2>\n

Time series forecasting<\/h3>\n

This method involves analysing historical data to identify trends and patterns that can be used to predict future values.<\/h5>\n
Time series analysis techniques include moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models.<\/h5>\n
Forecasting pandemic spread, diagnosis, and medication planning in healthcare<\/h5>\n
Time series analysis and forecasting became the key technique applied in healthcare to predict the spread of Covid-19.<\/h5>\n
It was used for transmission predictions, mortality ratios, the spread of the epidemic, and more.<\/h5>\n
Besides this obviously pertinent set of use cases, time series analysis and forecasting found their applications across almost all healthcare fields, from genetics to diagnosis and treatment.<\/h5>\n
There’s significant progress in both short- and long-term prognosis of medical conditions.<\/h5>\n
For instance, times series forecasting is used to predict possible death after heart attacks to apply preventive care.<\/h5>\n
Revenue cycle management and medical billing are at the other end of the treatment spectrum.<\/h5>\n
One of the use cases for time series forecasts is predicting expenditures on prescribed medications.<\/h5>\n

Tools to run time series forecasting include but not limited to:
\n<\/h3>\n
\n\n\t\t\t\t\t Amazon Forcast
\n\t\t\t<\/path><\/svg>
\n\t\t\t<\/path><\/svg>
\n\t\t\t\t\t\t<\/summary>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

Amazon Forecast is another managed tool to run time series forecasting with AWS. The system is capable of automatically analyzing data, finding key variables in it, and choosing the best-fit algorithm to apply. However, if you have enough data science experience, you can select from six built-in algorithms depending on your specific needs.<\/h5>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"
\n\t\t\t\t\t<\/details>\n

\n\n\t\t\t\t\t BigQuery and Vertex AI
\n\t\t\t<\/path><\/svg>
\n\t\t\t<\/path><\/svg>
\n\t\t\t\t\t\t<\/summary>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

TheIf you’re using the Google Cloud platform, you may access time series forecasting with its autoML tool called Vertex AI.<\/h5>\n
It comes with pre-configured models with customization tools.<\/h5>\n
In its core, Vertex AI leverages TensorFlow capabilities but requires far less data science expertise to operate, catering to average software engineers.<\/h5>\n
On top of that, you can explore, visualize, and prepare data for time series tasks using Google’s cloud data warehousing tool BigQuery as it comes integrated with Vertex AI.<\/h5>\n<\/details>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

Amazon Forecast is another managed tool to run time series forecasting with AWS. The system is capable of automatically analyzing data, finding key variables in it, and choosing the best-fit algorithm to apply. However, if you have enough data science experience, you can select from six built-in algorithms depending on your specific needs.<\/h5>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

TheIf you’re using the Google Cloud platform, you may access time series forecasting with its autoML tool called Vertex AI.<\/h5>\n
It comes with pre-configured models with customization tools.<\/h5>\n
In its core, Vertex AI leverages TensorFlow capabilities but requires far less data science expertise to operate, catering to average software engineers.<\/h5>\n
On top of that, you can explore, visualize, and prepare data for time series tasks using Google’s cloud data warehousing tool BigQuery as it comes integrated with Vertex AI.<\/h5>","protected":false},"comment_status":"open","ping_status":"closed","template":"","class_list":["post-41340","lesson","type-lesson","status-publish","hentry"],"yoast_head":"\nLesson 4: Quantitative forecasting methods - Time series forecasting - Certifeka-edu<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/certifeka-edu.com\/ar\/programs\/sample-course\/lessons\/lesson-4-quantitative-forecasting-methods-time-series-forecasting-2\/\" \/>\n<meta property=\"og:locale\" content=\"ar_AR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lesson 4: Quantitative forecasting methods - Time series forecasting - Certifeka-edu\" \/>\n<meta property=\"og:description\" content=\"Lesson 4: Forecasting and Predictive Analytics Time series forecasting This method involves analysing historical data to identify trends and patterns that can be used to predict future values. 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