Does your SEO strategy account for seasonality? If not, then your business could be at a disadvantage. Seasonal SEO takes all the basic considerations of a traditional SEO strategy but then looks at ways to drive conversions based on themes and a finite time period. Factoring seasonal influence into your overarching SEO strategy can help your business align your content and distribution strategy to meet the needs of your customers. In turn, this helps drive more qualified leads and increase conversions based on the intent of your users.
If you want to save your marriage, it might be best to avoid the holidays. New research has revealed that divorce rates appear to follow spike shortly after the summer and winter holidays. This seasonal pattern of divorce maybe driven by a tendency for couples to put off filing for a separation during times that are normally important to families. Divorce rates appear to follow a seasonal pattern according to new research. Married couples who watch pornography almost double their risk of divorce, according to research.
Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns, such as the unique behavior of specific days of the week in business data. We propose a multi-dimensional hierarchical model. Intermediate parameters for each seasonal period are first estimated, and a mixture of intermediate parameters is then taken, resulting in a model that successfully reflects the interactions between multiple seasonal patterns. Although this process reduces the data available for each parameter, a robust estimation can be obtained through a hierarchical Bayesian model implemented in Stan. Through this model, it becomes possible to consider both the characteristics of each seasonal period and the interactions among characteristics from multiple seasonal periods. Our new model achieved considerable improvements in prediction accuracy compared to previous models, including Fourier decomposition, which Prophet uses to model seasonality patterns. A comparison was performed on a real-world dataset of pallet transport from a national-scale logistic network.
As the search for exoplanets in the habitable zone or at the right distance from their host star to support water as well as other conditions necessary for life continues, one question remains -- how to confirm life actually exists on any of the identified worlds? Astronomers have discovered a number of exoplanets in the potentially habitable zone, but as these worlds sit hundreds to thousands of light years away, visiting and searching for alien life in person might not be the most viable option for space agencies. One possible solution revolves around looking for biosignatures or signs of life in the atmospheres of these worlds, such as the presence of oxygen or other gases necessary for life on Earth. Next-gen telescopes will look for these fingerprints while scanning the atmospheric composition of distant exoplanets, but that's just one piece of the puzzle. Even if these so-called "fingerprints of life" are identified, it is just a single measurement and might be misleading.
I have never been formally trained on how to deal with seasonality. But I wanted to take a moment to share my perspective based on experience, which I hope readers will find fairly straightforward. Some people use sales revenues in order to evaluate seasonal differences. I find it more desirable to analyze units sold if possible. A price increase resulting in slightly higher revenues does not in itself represent increased demand.