Time Series Forecasting with Machine Learning

TIMESTAMPS

0:00 Introduction
1:51 Defining Problem
2:50 Understanding the Data
3:18 Analyzing Data (Trend, Seasonality)
4:40 Traditional Timeseries Forecasting (ARIMA, Prophet)
6:01 Univariate & Multivariate Time series
8:15 Time series with Machine Learning
9:02 Types of Time series models
11:05 Machine Learning Vs. Traditional Time Series

REFERENCES
[1] Math behind Facebook prophet: https://medium.com/future-vision/the-math-of-prophet-46864fa9c55a
[2] Traditional time series analysis step by step: https://www.kaggle.com/freespirit08/time-series-for-beginners-with-arima
[2] Dealing with time series data: https://online.stat.psu.edu/stat510/lesson/1
[3] Catboost is slick : https://catboost.ai/docs/concepts/tutorials.html

#machine-learning

What is Time Series Forecasting?

In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting.

What is Time Series Analysis?

In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.

Here some examples where time series is used.

Business forecasting
Understand the past behavior
Plan future
Evaluate current accomplishments.

Components of time series :

Trend: Let’s understand by example, let’s say in a new construction area someone open hardware store now while construction is going on people will buy hardware. but after completing construction buyers of hardware will be reduced. So for some times selling goes high and then low its called uptrend and downtrend.
**Seasonality: **Every year chocolate sell goes high during the end of the year due to Christmas. This same pattern happens every year while in the trend that is not the case. Seasonality is repeating same pattern at same intervals.
Irregularity: It is also called noise. When something unusual happens that affects the regularity, for example, there is a natural disaster once in many years lets say it is flooded so people buying medicine more in that period. This what no one predicted and you don’t know how many numbers of sales going to happen.
Cyclic: It is basically repeating up and down movements so this means it can go more than one year so it doesn’t have fix pattern and it can happen any time and it is much harder to predict.

Stationarity of a time series:

A series is said to be “strictly stationary” if the marginal distribution of Y at time t[p(Yt)] is the same as at any other point in time. This implies that the mean, variance, and covariance of the series Yt are time-invariant.

However, a series said to be “weakly stationary” or “covariance stationary” if mean and variance are constant and covariance of two-point Cov(Y1, Y1+k)=Cov(Y2, Y2+k)=const, which depends only on lag k but do not depend on time explicitly.

#machine-learning #time-series-model #machine-learning-ai #time-series-forecasting #time-series-analysis

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Nora Joy

1604154094

Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
**
Services**
Product Engineering & Development
Re-engineering
Maintenance / Support / Sustenance
Integration / Data Management
QA & Automation
Reach us 917483546629

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

Services

Product Engineering & Development

Re-engineering

Maintenance / Support / Sustenance

Integration / Data Management

QA & Automation

Reach us 917483546629

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Nora Joy

1607006620

Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

ML and AI can offer high security in the transportation industry.
It offers high reliability of their services or vehicles.
The adoption of this technology in the transportation industry can increase the efficiency of the service.
In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

Identifying Diseases and Diagnosis
Drug Discovery and Manufacturing
Medical Imaging Diagnosis
Personalized Medicine
Machine Learning-based Behavioral Modification
Smart Health Records
Clinical Trial and Research
Better Radiotherapy
Crowdsourced Data Collection
Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

Fraud prevention
Risk management
Investment predictions
Customer service
Digital assistants
Marketing
Network security
Loan underwriting
Algorithmic trading
Process automation
Document interpretation
Content creation
Trade settlements
Money-laundering prevention
Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

Improved Unsupervised Algorithms
Increased Adoption of Quantum Computing
Enhanced Personalization
Improved Cognitive Services
Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Eldora Powlowski

1598034720

Flow-Forecast: A time series forecasting library built in PyTorch

Flow Forecast is a recently created open-source framework that aims to make it easy to use state of the art machine learning models to forecast and/or classify complex temporal data. Additionally, flow-forecast natively integrates with Google Cloud Platform, Weights and Biases, Colaboratory, and other tools commonly used in industry.

Background

In some of my previous articles I talked about the need for accurate time series forecasts and the promise of using deep learning. Flow-Forecast was originally, created to forecast stream and river flows using variations of the transformer and baseline models. However, in the process of training the transformers I encountered several issues related to finding the right hyper-parameters and the right architecture. Therefore, it became necessary to develop a platform for trying out many configurations. Flow forecast is designed to allow you to very easily try out a number of different hyper-parameters and training options for your models. Changing a model is as simple as swapping out the model’s name in the configuration file.

Another problem I faced was how to integrate additional static datasets into the forecasts. For river flow forecasting, there was a lot of meta-data such as latitude, longitude, soil depth, elevation, slope, etc. For this, we decided to look into unsupervised methods like autoencoders for forming an embedding. This spurred the idea of creating a generic way to synthesize embedding with the temporal forecast.

Using flow forecast

There are a couple easy resources to use to get started with flow-forecast. I recorded a brief introduction video back in May and there are also more detailed live-coding sessions you can follow. We also have a basic tutorial notebook that you can use to get a sense of how flow-forecast works on a basic problem. Additionally, there are also a lot more detailed notebooks that we use for our core COVID-19 predictions. Finally, we also have ReadTheDocs available for in depth documentation as well as our official wiki pages.

#machine-learning #pytorch #time-series-analysis #time-series-forecasting #deep-learning #deep learning