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Category: Statistics

Time Series Forecasting using ARIMA model

Time Series Forecasting using ARIMA model

ARIMA model: Introduction ARIMA is a widespread statistical technique for time series forecastingARIMA is an acronym. It stands for AutoRegressive Integrated Moving Average. This model captures a suite of diverse standard temporal structures in time series data.An ARIMA model is a class of statistical techniques for analyzing and forecasting time series data. To read more about time series analysis and its basic techniques please read this post. ARIMA is a widespread statistical technique for time series forecastingARIMA is an acronym….

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Stochastic Gradient Descent Technique with Example

Stochastic Gradient Descent Technique with Example

General idea In previous post, we talked about gradient descent optimization technique. Read full article here. In this post we will discuss about incremental/online version of gradient descent optimization algorithm Batch strategies, for example, restricted memory BFGS, which utilize the full preparing set to figure the following refresh to parameters at every emphasis will in general meet exceptionally well to nearby optima. They are likewise straight forward to get working gave a decent off the rack execution (for example minFunc)…

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Time series Analysis

Time series Analysis

Time series analysis contains techniques for breaking down time series information to separate significant measurements and different qualities of the information. Time series determining is the utilization of a model to anticipate future qualities dependent on recently watched qualities Objectives There are two fundamental objectives of time series analysis: 1.  Identifying the idea of the phenomenon spoken to by the grouping of observations 2.  Predicting (foreseeing future estimations of the time series variable). Both of these objectives necessitate that the…

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Exploratory Data Analysis: First milestone of data analysis

Exploratory Data Analysis: First milestone of data analysis

In statistics, exploratory data analysis (EDA) is a technique that analyze data to recapitulate their major features, frequently with visual approaches. Its is an initial step of data anlysis from experiment. Primarily EDA is for sighting what the data can express beyond the formal modeling or hypothesis testing job. EDA is different from initial data analysis (IDA) which emphases on glancing assumptions needed for model fitting and hypothesis testing, and managing missing values and making transformations of variables as required….

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