Figure 8: Workflow to forecast the monthly average sales in 2017 by an ARIMA (0,1,4) model using dynamic deployment. After forecasting, the trend and yearly seasonality are restored to the forecast residuals, and the accuracy of the forecasts is calculated. The workflow Forecasting and Reconstructing Time Series is available on the Hub. (Image Source: KNIME)<\/em><\/figcaption><\/figure>\nSummary<\/strong><\/p>\nTime series, be it sensor data showing the behavior of a tiny object nanosecond after nanosecond, macroeconomic data for the 20th century, or something in-between, have specific analytics techniques that apply to accessing, manipulating, and modeling steps.<\/p>\n
In this article, we have introduced you to the basics of analytics techniques for time series that help you to get started when you\u2019re working with time series data.<\/p>\n
References<\/strong><\/p>\n[1] Chambers, John C., Satinder K. Mullick, and Donald D. Smith. How to Choose the Right Forecasting Technique<\/em>. Harvard University, Graduate School of Business Administration, 1971.<\/p>\n[2] Hyndman, Rob J., and George Athanasopoulos. Forecasting: Principles and Practice<\/em>. OTexts, 2018.<\/p>\n Source: https:\/\/www.dataversity.net\/building-a-time-series-analysis-application\/<\/a><\/p>\n","protected":false},"author":1,"featured_media":806017,"template":"","meta":{"_eb_attr":"","type":"","auto_type":false,"post":"","stream":"","stream_url":"","waveform_data":[],"duration":0,"start":0,"end":0,"bpm":0,"downloadable":false,"download_url":"","purchase_title":"","purchase_url":"","post-count-all":0,"like_count":0,"download_count":0,"editor_note":"","copyright":"","captions":[],"sources":[]},"genre":[42022],"station_tag":[5591,3942,4045,4046,4047,3681,4811,4135,5128,5243,5179,4139,43974,4265,4791,4526,4244,4053,7101,5644,4442,5619,3946,3890,3724,3642,3792,3725,3938,5847,4170,3693,4991,4174,4175,4490,3772,4899,4068,4978,3950,5885,4072,5788,4491,7057,4076,6311,20848,3694,4185,3650,3850,4078,4186,43864,3908,3653,4195,3806,4255,4318,43612,4010,3654,457,3851,4089,3959,3658,475,4094,43589,5080,45435,3660,3706,3742,4354,43535,35995,17381,4208,3662,4355,3663,5870,4102,5114,3861,4018,3939,4259,5081,4109,43520,3972,4112,4433,4788,4117,4326,4460,3711,3778,3976,4222,4461,4548,3746,6396,3779,3714,3668,3749,4028,6082,5546,4033,3669,23431,4675,4128,5247,43573,3671,3750,7291,4129,4332,4586,3782,4631,3815,4926,4368,4518,3816,3935,43538,44390,3784,3717,3937,4668],"artist":[42038],"mood":[],"activity":[],"_links":{"self":[{"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/station\/806016"}],"collection":[{"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/station"}],"about":[{"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/types\/station"}],"author":[{"embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/users\/1"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/"}],"wp:attachment":[{"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/media?parent=806016"}],"wp:term":[{"taxonomy":"genre","embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/genre?post=806016"},{"taxonomy":"station_tag","embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/station_tag?post=806016"},{"taxonomy":"artist","embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/artist?post=806016"},{"taxonomy":"mood","embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/mood?post=806016"},{"taxonomy":"activity","embeddable":true,"href":"https:\/\/platodata.io\/wp-json\/wp\/v2\/activity?post=806016"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}