TIME_SERIES_ARTICLE.ipynb

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    "#Time Series Analysis\n",
    "\n",
    "**Definition**\n",
    "Time series analysis is a method of analyzing data points collected over a set time period. Through time series analysis, data analysts measure various data points at regular intervals. In this way, they can see how the points of data change with the passage of time, instead of gathering data points randomly or intermittently.\n",
    "\n",
    "In time series, we expect patterns to persist as we progress through time. Therefore, we follow a simple structure:\n",
    "\n",
    "1.Find the pattern\n",
    "2.Predict the future\n",
    "\n",
    "##Types of time series analysis\n",
    "Data analysts often use different types of time series analyses rather than constructing overly complex or crowded charts and graphs. Overfitting models or overly complex models may produce a lack of fit or may make it difficult for analysts to distinguish between real relationships and random errors.\n",
    "\n",
    "To that end, some of the most common times series analysis models include:\n",
    "\n",
    "1.**Exploratory analysis models**, which highlight and focus on primary features of the time series data in visual formats\n",
    "2.**Classification models**, which both identify and assign categories to data points\n",
    "3.**Descriptive analyses**, which identify patterns in time series data such as cycles or trends\n",
    "4.**Explanatory analyses**, which explore and understand the relationships of data within collected data sets (such as cause and effect)\n",
    "5.**Forecasting analysis models**, which are used to predict future data performance. Forecasting is always based on historical trends\n",
    "\n",
    "Examples of time series analysis models and techniques\n",
    "\n",
    "**Box-Jenkins ARIMA models**\n",
    "Box-Jenkins ARIMA models are univariate analysis models leveraged to understand one time-dependent variable, such as student performance over time, temperature over time, et cetera. ARIMA models are further used to predict future data points of those single, time-dependent variables.\n",
    "\n",
    "**Box-Jenkins multivariate models**\n",
    "Box-Jenkins multivariate models are, as the name suggests, helpful for analyzing more than one time-dependent variable. In this way, data analysts can examine how multiple variables change over time and potentially determine how those variables influence each other’s development.\n",
    "\n",
    "**Holt-Winters method**\n",
    "The Holt-Winters method is a useful time series analysis technique that relies on exponential smoothing. In short, the Holt-Winters method helps data analysts predict outcomes or future events. This method includes seasonality and cyclic data effects, thereby improving the accuracy of data forecasts.\n",
    "\n",
    "\n",
    "*How to make use of time series forecasting in python*\n",
    "\n",
    "Time series data analysis is used in a variety of contexts. Many organizations leverage time series analysis to understand the causes of different systemic patterns or detected trends over time. When used with a graph or other data visualization element, businesses can see things like seasonal trends and determine:\n",
    "\n",
    "1.Why those trends occur,\n",
    "2.Whether they can affect those trends for better results, and\n",
    "3.How the trends may change or stay the same in the future\n",
    "\n",
    "In fact, that last focus is why most organizations leverage time series analysis in the first place. Through acquiring and analyzing enough data, businesses and other organizations can perform time series forecasting. They can predict the likelihood or possibility of future events.\n",
    "\n",
    "###Steps to follow using time series forecasting\n",
    "1.Fitting the model\n",
    "2.Specifying the time interval\n",
    "3.Analyzing the results\n",
    "\n",
    "##Fitting the model\n",
    "\n",
    "Involves;\n",
    "        1.Scraping the data\n",
    "        2.Creating returns\n",
    "        3.Normalizing said returns\n",
    "        4.Separating the training and testing sets\n",
    "        \n",
    "##Specifying the time interval\n",
    "Next,we must specify the time interval for our time series forecast. It could be a day, a week, or whatever period we feel like making it. Of course, the starting date of the forecasted period is essentially the first one we don’t have values for. In other words, we’re looking for the first day after the end of the training set.\n",
    "\n",
    "##Analyzing the results\n",
    "This can be done by plotting the results over a specified period of time to analyze the forecasts.\n",
    "\n",
    "\n",
    "Time series analysis is an important part of data analytics, particularly when predicting future events or making wise business decisions. Data analysts need to know how to perform time series analysis to better serve their clients and to understand how data points can evolve more thoroughly than by simply observing beginning and end data points on a graph.\n"
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