If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We are adding Time Series Forecasting with the revolutionary amazing'Prophet' package with Exploratory v3.3! If not, then you can forecast the future and will be rich but so will everybody else. A part of the reason why it's hard is that the things like stock prices can be dramatically changed by many different conditions and irrational human behaviors influence the outcome a lot while such forecasting algorithms tend to build models based on the past data and with some reasonable logics behind. But it could be relatively easier for some other areas. For example, the population, GDP, or alcohol consumption of the United States can be forecasted relatively easier because these values won't dramatically change, so we can possibly forecast the long-term trend and that's what most of the economists at governments and institutions have been showing reasonable results.
Time series forecasting is ubiquitous in the modern world. Applications range from health care to astronomy, include climate modelling, financial trading and monitoring of critical engineering equipment. To offer value over this range of activities we must have models that not only provide accurate forecasts but that also quantify and adjust their uncertainty over time. Furthermore, such models must allow for multimodal, non-Gaussian behaviour that arises regularly in applied settings. In this work, we propose a novel, end-to-end deep learning method for time series forecasting. Crucially, our model allows the principled assessment of predictive uncertainty as well as providing rich information regarding multiple modes of future data values. Our approach not only provides an excellent predictive forecast, shadowing true future values, but also allows us to infer valuable information, such as the predictive distribution of the occurrence of critical events of interest, accurately and reliably even over long time horizons. We find the method outperforms other state-of-the-art algorithms, such as Gaussian Processes.
There is an aggregated measure represented by a variable A, modeled as a time series from a process. There was a need forecast A and also to find out the historical amount of data of A that is the best reflector of future values of A (as there was a data storage capacity issue). Using a combination of sliding window regression technique and ARIMA, it is found that the size of the sliding window out of different window sizes tried, is 100 (as it gave lesser MAPE than the rest of the ones). So the past 100 values of A is a better reflector of future. "A" aggregation comes from B and C of the same process, such that A B C and there is a need to predict these variables as a percentage of A. B and C are modeled as a time series.
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as "Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as "epoch" in deep learning, has no effect on the performance of the trained forecast model and it exhibits a truly random behavior.
Most of us would have heard about the new buzz in the market i.e. Many of us would have invested in their coins too. But, is investing money in such a volatile currency safe? How can we make sure that investing in these coins now would surely generate a healthy profit in the future? We can't be sure but we can surely generate an approximate value based on the previous prices. Time series models is one way to predict them.
I just read two articles that claim that Python is overtaking R for data science and machine learning. From user comments, I learned that R is still strong in certain tasks. I will survey what these tasks are. The first article by Vincent Granville from DSC uses proxy metrics (as opposed to asking the users). He uses statistics from Google Trends, Indeed job search terms, and Analytic Talent (DSC job database) to conclude that Python has overtaken R. One is led to ask if one group of users (say Python's) is a more active googler.
Business forecasting case study example is one of the popular case studies on YOU CANalytics. Originally, the time series analysis and forecasting for the case study were demonstrated on R in a series of articles. One of the readers, Anindya Saha, has replicated this entire analysis in Python. You could read this python notebook at this link: Python Notebook for Forecasting.
Until now, the only sequence data we've covered has been text data, such as the IMDB dataset and the Reuters dataset. But sequence data is found in many more problems than just language processing. In all the examples in this section, you'll play with a weather timeseries dataset recorded at the Weather Station at the Max Planck Institute for Biogeochemistry in Jena, Germany.
Understanding timely patterns/characteristics in data are becoming very critical aspect in analyzing and describing trends in business data . Example Use case 1: Fitness device market is built around buy people to help track fitness related data to monitor effectiveness of their fitness exercises. Example Use Case 2: Sales growth of a product over period of time is a good indicator of sales performance of a product manufacturing company. A typical time series model can exhibits different patterns. Therefor it is important to understand components of a time series in detail .