We show that for thousands of years, humans have concentrated in a surprisingly narrow subset of Earth’s available climates, characterized by mean annual temperatures around ∼13 °C. This distribution likely reflects a human temperature niche related to fundamental constraints. We demonstrate that depending on scenarios of population growth and warming, over the coming 50 y, 1 to 3 billion people are projected to be left outside the climate conditions that have served humanity well over the past 6,000 y. Absent climate mitigation or migration, a substantial part of humanity will be exposed to mean annual temperatures warmer than nearly anywhere today.
All species have an environmental niche, and despite technological advances, humans are unlikely to be an exception. Here, we demonstrate that for millennia, human populations have resided in the same narrow part of the climatic envelope available on the globe, characterized by a major mode around ∼11 °C to 15 °C mean annual temperature (MAT). Supporting the fundamental nature of this temperature niche, current production of crops and livestock is largely limited to the same conditions, and the same optimum has been found for agricultural and nonagricultural economic output of countries through analyses of year-to-year variation. We show that in a business-as-usual climate change scenario, the geographical position of this temperature niche is projected to shift more over the coming 50 y than it has moved since 6000 BP. Populations will not simply track the shifting climate, as adaptation in situ may address some of the challenges, and many other factors affect decisions to migrate. Nevertheless, in the absence of migration, one third of the global population is projected to experience a MAT >29 °C currently found in only 0.8% of the Earth’s land surface, mostly concentrated in the Sahara. As the potentially most affected regions are among the poorest in the world, where adaptive capacity is low, enhancing human development in those areas should be a priority alongside climate mitigation.
Karpet is a tiny library with just a few dependencies for fetching coins/tokens metrics data the internet.
It can provide following data:
- coin/token historical price data (no limits)
- google trends for the given list of keywords (longer period than official API)
- twitter scraping for the given keywords (no limits)
- much more info about crypto coins/tokens (no rate limits)
You will learn in this post how to:
- decompose double-seasonal time series
- detrend time series
- model and forecast double-seasonal time series with trend
- use two types of simple regression trees
- set important hyperparameters related to regression tree
This web site contains notes and materials for an advanced elective course on statistical forecasting that is taught at the Fuqua School of Business, Duke University. It covers linear regression and time series forecasting models as well as general principles of thoughtful data analysis.
The time series material is illustrated with output produced by Statgraphics, a statistical software package that is highly interactive and has good features for testing and comparing models, including a parallel-model forecasting procedure that I designed many years ago.
The material on multivariate data analysis and linear regression is illustrated with output produced by RegressIt, a free Excel add-in which I also designed. However, these notes are platform-independent. Any statistical software package ought to provide the analytical capabilities needed for the various topics covered here.
This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price.
The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep neural networks.
When you are presented with a new time series forecasting problem, there are many things to consider.
The choice that you make directly impacts each step of the project from the design of a test harness to evaluate forecast models to the fundamental difficulty of the forecast problem that you are working on.
It is possible to very quickly narrow down the options by working through a series of questions about your time series forecasting problem. By considering a few themes and questions within each theme, you narrow down the type of problem, test harness, and even choice of algorithms for your project.
In this post, you will discover a framework that you can use to quickly understand and frame your time series forecasting problem.