Time series forecasting is the process of predicting future using current and past data.A time series is a series taken at successive intervals. Time series consist of data taken over successive equally spaced points in time. These intervals can be daily,weekly, monthly, yearly etc.
The major difference between the time series and the rest is that time series is data dependent, in time series we use dependent variables to predict the target variable(Future data), while in normal models independent variables are use to predict dependent variable or target variable.
Time series usually contains trend and seasonality , so normal machine learning techniques won’t be much effective in dealing with time series forecasting problems, More importantly machine learning models such as regression will be most effective when the data points are independent of each other , but in time series the case is opposite. The accuracy of the forecasting model is important as it helps to deal with uncertainty in the data, Many financial companies depends on forecasting models, a better forecasting model would leads to gain and poorer one will lead to loss, so selecting a perfect model with the correct hyperparameters will be the most important task. 1. Getting Started with Building Realtime API Infrastructure 2. How I used machine learning as inspiration for physical paintings 3. MS or Startup Job — Which way to go to build a career in Deep Learning? 4. TOP 100 medium articles related with Artificial Intelligence Precipitation is useful parameter for accessing vital-water resource , agriculture , ecosystem and hydrology. In forecasting rainfall, satellite images, ground observation stations and weather balloons were used mostly. There is also radar imaging technology, In recent years use of artificial intelligence has attracted considerable attention. Different machine learning techniques(Regression, ARIMA,Artificial Neural Net, LSTM, Resorvoir computing)and there results are compared and analysed in the upcoming articles. The link for the next article is attached below
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