Time Series Forecasting is an integral part of machine learning which is used in many applications such as weather forecasting, stock prices forecasting, resource allocation, business planning, and a lot more. Time Series Forecasting a bit hard to handle because it is directly related to the time component so that it may result in a lot of prediction problems.
Normally datasets of machine learning are independent of the time factors. Predicted information may differ from the actual data, which will be only available in the future. There is a chance of error associated with future predictions, but important observations are always taken into account.
Time Series Forecasting deals with developing predictive mathematical models for various applications by assuming and analyzing time datasets. time datasets are different from others in such a way that it always shares an explicit dependence with time dimensions which will result in varying observations. Time Series Forecasting structures with two main factors such as time series analysis and time series forecast.
Time series analysis deals with understanding and analyzing datasets which will help the observer for making better predictions. But it is time-consuming and it also requires expertise from individuals and since it is dependent on the future, the success rate of all these procedures is unknown.
When it comes to choosing the time series forecasting methods there are certain things that are considered such as hidden assumptions regarding data and some other outside factors that affect the trends.
It is the simplest way of developing models that may end with accurate results.
Regressive models such as polynomial, linear, or exponential models are created for time series forecasting. By differentiating among a lot of observations and assumptions, a trend model is developed.
Time series smoothing methods are used to develop fine models that can deal with changes in data with respect to time. Unlike other noisy models, smoothing reduces noise by taking the mean of observations over several time periods.
The main techniques involved in time series forecasting are time series analysis and time series forecasting.
Time series analysis is a phase of Time Series forecasting which results in developing mathematical models based on selecting or describing a time series which is observed for finding the underlying cause. This is a procedure that involves assuming data and deciphering the observed time series into its own constituents. The way how data is available and interpreted in the descriptive model adds more value to it. The developed model should address the issues of the problem domain with appropriate solutions.
Time series forecasting comprises developing predictive models based on past datasets for future forecasting. It is done by handling historic data statistically. Outcomes in the future are unknown so that it is estimated with available data about already happened incidents. Future series of values are predicted completely based on past series of values.
Time series forecasting follows a set of instructions called an algorithm for developing different mathematical models. The models were developed in python and r.
In python, mainly time series forecasting methods are classified into 10.
It generates output as linear functions of observations for the time series. Autoregression will be done at different levels. It will univariate time datasets independent from seasonal and trend factors.
It will calculate the moving average of time series by taking an average of the series of observations. It generates output as a linear function of residual errors
It is the combination of regression and moving average model. It generates the output as a linear function of residual errors and observations
It will generate the output as a linear function of differenced observations and residual errors. This method combines autoregression and moving average with differencing preprocessing to make the output sequence stationery, which is called integration.
It generates the output sequence as linear function of the differenced observations, errors, differenced seasonal observations, and seasonal errors at prior time steps.
It is an extended version of the SARIMA model in which creating models for exogenous variables are also included.
It is an AR model in which AR is categorized into different parallel series of time.
In here ARMA will be generalized into various parallel time series.
It is the extended version of VARMA model which also includes modeling of exogenous variables.
It results in an output sequence of linear function of observations which will be depended on seasonal and trend components.
Mainly there are 4 methods used in R programming language for time series forecasting.
The simple naïve method uses the recent observation values whereas in the seasonal naïve method observations of the past season are used to predict the future seasonal values.
A weighted average of previous observations was considered by giving major focus to recent ones.
It is used for multiple seasonality. TBATS is the extended version of BATS that allows multiple integer seasonality cycles.
ARIMA will generate the output as a linear function of differenced observations and residual errors SARIMA comes up along with differenced seasonal observations and seasonal errors.
For time series forecasting excel mainly uses exponential smoothing, in which the weighted average of observations is considered by giving extra importance to the recent ones.
Time series forecasting is very important for many application domains. The success rate of predicting the future is not easy to calculate because of its direct dependency on time constrain. Future predictions always have chances of errors. A lot of active research works are going on on this subject during several years. Many important models have been proposed for improving the accuracy and efficiency of time series modeling and forecasting. Time series forecasting always have room for improvement in order to yield more accurate data
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