FB Prophet is not always the most suitable time series forecasting model

I have written a series of posts about a univariate dataset of shampoo sales dating back from 1901 to 1903. This series of posts would not be complete, however, without a post on Facebook Prophet, an open source time series forecasting model. The finale of this series of posts, therefore, attempts to shed light on the forecasting of the shampoo dataset using Facebook Prophet, which is based on based on decomposable (trend+seasonality+holidays) models.

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Using Linear Regression to predict the Dow Jones Industrial Average Index

The stock market is filled with individuals who know the price of everything, but the value of nothing.” — Fischer“A prediction about the direction of the stock market tells you nothing about where stocks are headed, but a whole lot about the person doing the predicting.”

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And this, in a nutshell’s nutshell, is “Machine Learning”

Combining Part 1 and 2: Probably imagining a robot or terminator when asking about machine learning, in reality, machine learning is beyond and involved in almost all application you can imagine in our today world. Think of spam filter in your email, voice, face and fingerprint recognition on your phone, your Siri on iPhone, google assistance on android device etcetera have an iota of machine learning.

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Google trained a trillion-parameter AI language model

Parameters are the key to machine learning algorithms. They’re the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. For example, OpenAI’s GPT-3 — one of the largest language models ever trained, at 175 billion parameters — can make primitive analogies, generate recipes, and even complete basic code.

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What is a Time Series GAN?

This post was originally published by Sejuti Das at Analytics India Magazine Identifying anomalies in time series data can be daunting, thanks to the vague definition of anomalies, lack of labelled data, and highly complex…

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All Machine Learning Algorithms you should know in 2021

Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.

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Five trends to look for in governing data, in 2021, for digital-driven business outcomes

To date, the organizations have focused on formalizing data consumption practices through distribution technology, access-based delivery mechanisms for analytics, and AI functions. However, with data protection laws and positive awareness across the world, firms have extended the formalization to data collection management. This in-fact is the first life-cycle stage of data.

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Top 12 Data Structure Algorithms to implement in practical applications in 2021

The new year is coming and this new year we encourage you to check out the practical scenarios of famous algorithms instead of learning them just for the sake of a job. In this blog, we will discuss some practical implementations of these algorithms in the real world. 
No matter if you’re a fresher or an experienced person, you will find it interesting to read. This article will refresh the memories of experienced programmers.  

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Breakthroughs in Time Series Forecasting at Neurips 2020

A deep dive into the latest literature in time series forecasting and how you can use them for your business use cases. This year at the Neural Information Processing Conference, authors published a number of new papers focusing on time series forecasting and classification. Here I will briefly review their major contributions as well as discuss their implementation and our timeline for porting them to our deep learning for time series forecasting framework flow-forecast.

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What is Model complexity? Compare Linear Regression to Decision Trees to Random Forests

A machine learning model is a system that learns the relationship between the input (independent) features and the target (dependent) feature of a dataset to be useful in making predictions in the future. In this article, we are going to test the effectiveness of 3 popular models that vary in complexity.

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Informer: LSTF (Long Sequence Time-Series Forecasting) Model

Time series forecasting is the most complex technique to solve and forecast with the help of traditional methods of using statistics for time series forecasting the data. But now as the neural network has been introduced and many CNN-based time series forecasting models have been developed, you can see how accurate and easy it became to predict future values based on historical time-series data points. Long short term memory(LSTM) is the one which is used for long-term forecasting. But there are many problems with LSTM which leads to further research in LSTF…

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What is Artificial Neural Network and how is it useful ?

An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of Artificial Intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.

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Test for existence of a Trend in a Time Series

A time series comprises four major components. A trend. A seasonal component. A cyclic component. And a stochastic/ random component. All these components may or may not be present in a time series. Therefore, before estimating these components, we need to first check for their existence. If they are present then we can move forward with their estimation. This article explains the Relative Order Test for testing the existence of a trend.

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