Machine Learning - Time Series Part 2
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Detailed notes about the working of Time Series Simple Moving Averages as well Exponential Moving Averages and other data smoothening techniques.
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Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
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Learn more at: https://www.simplilearn.com/
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The trend component represents long-term increases or decreases in the data. The seasonal component captures regular fluctuations that occur with a fixed periodicity, such as monthly or yearly patterns. The irregular component represents stationary random fluctuations.
Time series can be decomposed additively, by adding the trend, seasonal, and irregular components, or multiplicatively, by multiplying them. The appropriate method depends on whether seasonal fluctuations vary with the overall level of the series. Decomposition aims to produce stationary irregular components that can be modeled.
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Time series analysis involves analyzing data collected over time. A time series is a set of data points indexed in time order. The key components of a time series are trends, seasonality, cycles, and irregular variations. Trend refers to the long-term movement of a time series over time. Seasonality refers to periodic fluctuations that occur each year, such as higher sales in winter. Cyclical variations are longer term fluctuations in business cycles. Irregular variations are random, unpredictable fluctuations. Time series analysis is important for forecasting, economic analysis, and business planning. Common methods for analyzing time series components include moving averages, least squares regression, decomposition models, and harmonic analysis.
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This document discusses stationarity in time series analysis. It defines stationarity as a time series having a constant mean, constant variance, and constant autocorrelation structure over time. Non-stationary time series can be identified through run sequence plots, summary statistics, histograms, and augmented Dickey-Fuller tests. Common transformations like removing trends, heteroscedasticity through logging, differencing to remove autocorrelation, and removing seasonality can be used to make non-stationary time series data stationary. Python is used to demonstrate identifying and transforming non-stationary time series data.
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(1) Forecasting models use historical time series data to predict future trends and patterns. Quantitative forecasting methods are most relevant for this course.
(2) Key components of time series include trends, cyclical patterns, seasonality, and irregular fluctuations. Trend lines show gradual shifts, while cyclical components involve recurring patterns above and below trends.
(3) Common smoothing methods like moving averages, weighted averages, and exponential smoothing filter out irregular fluctuations to forecast stable time series without strong cycles. These methods are less effective for series with seasonal or cyclical components.
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The document discusses adjusted exponential smoothing, a time series forecasting model that accounts for trends in data. It defines the model's equation and components, illustrates how to apply it using sample demand data for portable CD players, and provides an example of how a company could use the model to forecast demand for financial planning purposes. The model smooths random fluctuations in data and provides more accurate forecasts by adjusting for trends over time.
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The document discusses seasonal adjustment methods for time series forecasts. It defines seasonality and explains common causes of seasonal patterns. The main seasonal adjustment method described is a four-step process: 1) forecast demand values, 2) calculate demand/forecast ratios, 3) average ratios to determine seasonal indices, 4) adjust forecasts by multiplying them by seasonal indices. An example is provided to illustrate applying this method to quarterly widget demand data.
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Below topics are explained in this " Time Series in R presentation " -
1. Introduction to ARIMA model
2. Auto-correlation & partial auto-correlation
3. Use case - Forecast the sales of air-tickets using ARIMA
4. Model validating using Ljung-Box test
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
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5. Anyone with a genuine interest in the data science field
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Learn more at: https://www.simplilearn.com/
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Linear regression and logistic regression are two machine learning algorithms that can be implemented in Python. Linear regression is used for predictive analysis to find relationships between variables, while logistic regression is used for classification with binary dependent variables. Support vector machines (SVMs) are another algorithm that finds the optimal hyperplane to separate data points and maximize the margin between the classes. Key terms discussed include cost functions, gradient descent, confusion matrices, and ROC curves. Code examples are provided to demonstrate implementing linear regression, logistic regression, and SVM in Python using scikit-learn.
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- 1. Machine Learning - VI Time Series - II
- 2. Time Series: Simple Moving Average Types: • Simple Moving Averages • Exponential Moving Averages A simple moving average is formed by computing the average value of a series over a specific number of periods. data("AirPassengers") View(AirPassengers) AirPassengers1=AirPassengers plot(AirPassengers1) library(TTR) AirPassengers1_smoothened<- SMA(AirPassengers1,n=8) #n= number of periods to average over plot.ts(AirPassengers1_smoothened) Rupak Roy Before After
- 3. Time Series: Exponential Moving Exponential Moving Averages: • The weighting applied to the values depends on the number of periods in the moving average. • Further we can do predictive modeling and forecast future time points with exponential smoothening averages by using HoltWinters() function. Rupak Roy
- 4. Time Series: Exponential Moving data("EuStockMarkets") euro_stocks<-EuStockMarkets euro_stocks1<-as.data.frame(EuStockMarkets) euro_stocks2<-euro_stocks1$DAX #create time series data eu_stocks_ts3<-ts(euro_stocks2,frequency=12, start = c(1991,1), end = c(1998)) eu_stocks_ts3 plot(eu_stocks_ts3)) #Log transformation(Reducing the spread of data) logTimeSeries<-log(eu_stocks_ts3) plot.ts(logTimeSeries) #Apply Exponential smoothening average- Holt winters TimeSeries.exp<-HoltWinters(logTimeSeries,beta=FALSE) TimeSeries.exp plot(TimeSeries.exp) #We can observe the accuracy of the HoltWinters output is very close to the original values means it is able to predict with high accuracy Rupak Roy
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