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How To Convert Non-Stationary To Stationary Time Series Data.?
How To Convert Non-Stationary To Stationary Time Series Data? Table Of Contents: Introduction. Detrending. Differencing. Transformation. (1) Introduction ‘Stationarity’ is one of the most important concepts you will come across when working with time series data. A stationary series is one in which the properties – mean, variance and covariance, do not vary with time. Let us understand this using an intuitive example. Consider the three plots shown below: In the first plot, we can see that the mean varies (increases) with time which results in an upward trend. Thus, this is a non-stationary series. For a series to be
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Why Time Series Data Need To Be Stationary?
Why Time Series Data Needs To Be Stationary ? (1) Reason-1 In Time Series data we know that the current data value is dependent on the previous data values. Which means there is a relationship between previous values and current values. If the data set is non-stationary the mean and variance of the data will change over time. As time series uses only one variable for prediction, with changing mean and variance it will be difficult to calculate the relationship between past values. Hence Time Series demands the Stationarity of data. (2) Reason-2 What quantities are we typically interested in
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Time Series Analysis
Time Series Analysis Table Of Contents: What Is Time Series Analysis? How to Analyze Time Series? Significance of Time Series Components of Time Series Analysis What Are the Limitations of Time Series Analysis? Data Types of Time Series Methods to Check Stationarity Converting Non-Stationary Into Stationary Moving Average Methodology Time Series Analysis in Data Science and Machine Learning What Is an Auto-Regressive Model? Implementation of Auto-Regressive Model Implementation of Moving Average (Weights – Simple Moving Average) Understanding ARMA and ARIMA Understand the signature of ARIMA Process Flow (Re-Gap) Conclusion Frequently Asked Questions (1) What Is Time Series Analysis Time series
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Linear Regression – Evaluation Matrices
Linear Regression – Evaluation Metrices Table Of Contents: Mean Absolute Error. Mean Squared Error. Root Mean Squared Error. R – Squared Error Adjusted R – Squared Error. (1) Mean Absolute Error. Mean Absolute Error calculates the average difference between the calculated values and actual values. It is also known as scale-dependent accuracy as it calculates error in observations taken on the same scale. MAE provides a straightforward measure of the model’s accuracy, as it represents the average magnitude of errors without considering their direction. Formula: Example: To calculate the MAE, we follow these steps: Calculate the absolute differences between the
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Linear Regression – Problem Statements
Linear Regression – Problem Statements (1) Marketing Promotion TV vs Radio vs Social Media Each row corresponds to an independent marketing promotion where the business uses TV, social media, radio, and influencer promotions to increase sales. The features in the data are: TV promotional budget (in “Low,” “Medium,” and “High” categories) Social media promotional budget (in millions of dollars) Radio promotional budget (in millions of dollars) Sales (in millions of dollars) Influencer size (in “Mega,” “Macro,” “Micro,” and “Nano” categories) (2) USA Optimal Product Price Prediction Dataset This dataset contains product prices from Amazon USA, with a focus on price
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Gradient Descent Algorithm
Gradient Descent Algorithm Table Of Contents: What Is Gradient Descent? Algorithm Requirements. What Is Gradient? How Gradient Descent Algorithm Works? (1) What Is Gradient Descent? Gradient descent (GD) is an iterative first-order optimisation algorithm, used to find a local minimum/maximum of a given function. This method is commonly used in machine learning (ML) and deep learning (DL) to minimise a cost/loss function (e.g. in a linear regression). Gradient Descent algorithm is the backbone of Machine Learning because whatever the loss function you give, it will find out its local minimum value. (2) Algorithm Requirements. The Gradient Descent algorithm does not work for all functions. There
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Linear Regression: OLS Technique
Linear Regression – OLS Technique. (1) Introduction. The Linear Regression model’s main objective is to find the best-fit line that will closely pass through all the points to minimize the loss. The question here is how to find out the best-fit line. We need some mathematical proof that this is my best-fit line. To solve this problem we have two techniques. Ordinary Least Square Technique. Gradient Descent Technique. (2) Ordinary Least Square Technique. In case of OLS technique we directly use the formula for ‘m’ and ‘b’ to derive the best fit line equation. Here in this vlog we will
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Extreme Gradient Boosting – Regression Algorithm
Extreme Gradient Boosting – Regression Algorithm Table Of Contents: Example Of Extreme Gradient Boosting Regression. Problem Statement: Predict the package of the students based on CGPA value. Step-1: Build First Model In the case of the Boosting algorithm, the first model will be a simple one. For the regression case, we have considered the mean value to be our first model. Mean = (4.5+11+6+8)/4 = 29.5/4 = 7.375 Model1 output will always be 7.373 for all the records. Step-3: Calculate Error Made By First Model To calculate the error we will do the simple subtraction operation. We will subtract the
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Gradient Boosting – Classification Algorithm
Gradient Boosting – Classification Algorithm Table Of Contents: Example Of Gradient Boosting Classification. Problem Statement: Whether the student will get placement or not is based on CGPA and IQ. Step-1: Build First Model – Calculate Log Of Odds. In the case of the Boosting algorithm, the first model will be a simple one. For the regression case, we have considered the mean value to be our first model. But it will not make any sense in the case of classification. Hence we will consider Log(odds) as our mathematical function for the first model. Log(Odds) The odds of an event happening
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Cat Boost Algorithm
Cat Boost Algorithms Table Of Contents: What is the CatBoost Algorithm? Features Of CatBoost Algorithm. Is tuning required in CatBoost? When and When Not to Use CatBoost (1) What Is The Cat Boost Algorithm? The term CatBoost is an acronym that stands for “Category” and “Boosting.” Does this mean the “Category’ in CatBoost means it only works for categorical features? The answer is, “No.” According to the CatBoost documentation, CatBoost supports numerical, categorical, and text features but has a good handling technique for categorical data. The CatBoost algorithm has quite a number of parameters to tune the features in the processing stage. “Boosting” in CatBoost refers to
