How to use Time Series forecasting in Machine Learning

A time series is a sequence of observations recorded over a certain period. A simple example of time series is how we come across different temperature changes day by day or in a month. The tutorial will give you a complete sort of understanding of what is time-series data, what methods are used to forecast time series, and what makes time series data so special a complex topic in the field of data science.



Timeseries forecasting in simple words means to forecast or to predict the future value(e.g.-stock price) over a period of time. There are different approaches to predict the value, consider an example there is a company XYZ records the website traffic in each hour and now wants to forecast the total traffic of the coming hour.

A different person can have a different perspective like one can say find the mean of all observations, one can have like take mean of recent two observations, one can say like give more weightage to current observation and less to past, or one can say use interpolation. There are different methods to forecast the values. While Forecasting time series values, 3 important terms need to be taken care of and the main task of time series forecasting is to forecast these three terms.

1) Seasonality - Seasonality is a simple term that means while predicting a time series data there are some months in a particular domain where the output value is at a peak as compared to other months. for example if you observe the data of tours and travels companies of past 3 years then you can see that in November and December the distribution will be very high due to holiday season and festival season. So while forecasting time series data we need to capture this seasonality.

2) Trend - The trend is also one of the important factors which describe that there is certainly increasing or decreasing trend time series, which actually means the value of organization or sales over a period of time and seasonality is increasing or decreasing.

3) Unexpected Events - Unexpected events mean some dynamic changes occur in an organization, or in the market which cannot be captured. for example a current pandemic we are suffering from, and if you observe the Sensex or nifty chart there is a huge decrease in stock price which is an unexpected event that occurs in the surrounding. Methods and algorithms are using which we can capture seasonality and trend But the unexpected event occurs dynamically so capturing this becomes very difficult.