Time Series Forecasting explained with Tharun!

Hi everyone,  I am Tharun and today I am going to discuss benchmark methods for Forecasting that are simple to understand and effective for some Time-series problems. So let's start the Topic! Benchmark Methods for Forecasting:  Average Method  Naive Method   Seasonal Naive Method  Drift Method Average Method : In the Average Method, the forecasts for all future values are equal to the average of past data. y T + h represents forecasted values based on historical data . h represents the forecast horizon you want to forecast  (next 6 months, 1year, etc ) y1, y2,......, yT represents historic data points. So let's take an example dataset and discuss this method. I have taken a dataset that contains Monthly milk production from the year 1962 to 1975. so let's have a quick look into the head of the dataset. Now let's plot the dataset: Now I split the dataset into train and test as shown below. ...

Hi everyone, I hope everyone is Fine. I am Tharun and today I am going to discuss the most important topic in Stationarity. I already discussed the basics of Stationarity in my previous post please refer to it if you haven't gone through. Let's start the topic without wasting time. In order to confirm whether the Time Series is Stationary or not. We need to confirm three aspects. Constant Mean. Constant Variance. No Autocorrelation.     I discussed the first two concepts and why it is important to check Stationarity before building a model for the time series clearly in the previous post. Today I am going to discuss in detail about Autocorrelation. Before diving into Autocorrelation lets discuss the basics of Correlation and Time Lags. Correlation : Correlation describes the linear relationship between two variables.  Let's say x and y are two variables then Correlation coefficient ( r ) is given by: r can take values from -1 to 1....

Hi everyone, I am Tharun and today I am going to discuss the basic statistics to be observed while working with Time Series data. Trend Seasonality Cyclic Mean Variance Stationarity  I have taken the airlines dataset which contains the number of passengers travelled from the year 1949 to 1961 and discuss the topics briefly! I am sharing the head of the dataset for your inference. I plotted time series using pandas as shown below: We will examine this graph and let's discuss the statistics mentioned.   Trend :   A trend exists when there is a long-term increase or decrease in the data. It does not have to be linear. In the above series, we can clearly see an upward trend along with the time. This indicates the number of passengers who are taking airlines increased with time from 1949 to 1961. Seasonality :  A seasonal pattern occurs when a time series is affected by seasonal factors such as the time of the year or the day of the we...

Hi everyone I hope everyone is fine. Today I am going to discuss the basics and importance of Time Series forecasting and how it is extensively used in many industries.  Forecasting is carried out by everyone in day to day life. Some practical examples  A person may forecast the expenses based on the plans and bills in a particular month.  A project manager will forecast the time required for the completion of a project based on the complexity involved. These things are easier to forecast since we have a clear idea of facts with us. In simple terms, the time of the sunrise tomorrow morning can be forecast precisely. On the other hand, tomorrow’s lottery numbers cannot be forecast with any accuracy. The predictability of an event or a quantity depends on several factors including how well we understand the factors that contribute to it how much data is available whether the forecasts can affect the thing we are trying to forecast Like an...