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Time series analysis for machine learning

A statistical technique that deals with time-series data is referred to as the time series analysis. It can be used to see how a given data changes over some time. This data can be anything like an asset, security, or any economic variable. Time series is a sequence of numerical data in order. It is also defined as a sequence taken at equal periods. Thus it is a sequence of discrete-time data. For investment, a time series tracks the ups and downs of the chosen data during a specific period, such as security’s price. It can be taken on any data that has changed over time and that still changes. 

Time series analysis can also be useful to examine the changes associated with that chosen data point and comparing to shifts in other variables over that same period.

Example for time series analysis-

Suppose you wanted to analyze a time series of daily opening stock prices for any given stock for two years. For this task, you would maintain a list of opening prices of each day for the past two years. And this list would be the two-year daily opening price time series for your stock. This method is manual and may require some time to maintain that data. If you want, you might analyze time-series data with available technical analysis tools to know what changes have taken place in the past year for opening prices of a stock.

Time series are studied to interpret a phenomenon, identify a trend, and determine its future values. Some real-world example of time series analysis is that you must have heard people discussing the prices of many items that have decreased or increased when compared to past years. Another example would be the rate of interest fluctuating in banks and different for various loans. 

All these types of data are time-series data and have to go through analysis for the forecast.

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There are three types of time series data:

  1. Time series data: it is a set of observational values taken by a variable at different times.
  2. Cross-sectional data: it is the data values of one or more than one variable taken at the same point of time.
  3. Pooled data: it is a combination of time series data and cross-sectional data. 

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Time series forecasting-

Time series forecasting uses historical values and associated patterns to determine the future of that data. Most of the time, this relates to trend analysis, cyclical fluctuations analysis, and issues of seasonality. But even after using this technique, success is not guaranteed.

Methods for analysis-

Time series analysis techniques are divided into non-parametric and parametric methods.

  1. The parametric method assumes that the stationary stochastic process has a specific structure that can be further defined using a small number of arguments. Here, the task is to approximate the parameters of that module.
  2. In the non-parametric method, estimation of the spectrum of the process is done without presuming that the process has any definite structure.

Sometimes, time series analysis may also be categorized into non-linear and linear, and multivariate and univariate.

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Implementation of time series analysis – machine learning-

We learned that many advanced tools help us in time series analysis. Some of the tools are ML, Lot, etc.

Nowadays, machine learning is a well-known field and a very powerful technique in imaging, speech, and ordinary language processing for available datasets.

As a time series does not have typically interpreted datasets and it requires advanced sorting algorithms that can facilitate it to study time-dependent forms from different datasets, machine learning has many algorithms and many problems involving time aspects for prediction, which can do this task easily. There is a number of models and methods that can be used for time series forecasting. Some of them are discussed below-

ML methods to forecast time series:-

  1. In the univariate time-series forecasting approach, the problem contains two variables and one of them is time and another one is the field that we are looking into.
    1. An example of this would be that suppose you want to determine the mean price of diesel in the upcoming week, one argument is time (one week) and another argument is diesel.
    2. Another example, measuring the heart rate of a person per minute by making use of past observations only. Here, one argument is time (one minute) and another one is heart rate.
  2. Multivariate time-series forecasting method, the problems contain many variables as the fixed time and others are multiple parameters.

ML models for time-series forecasting:-

  1. ARIMA model- it is an amalgamation of three different models AR, MA, and I, where 

       ‘AR’- it reflects the growing variable of interest.

        ‘MA’- it reflects the regression error that occurred is the linear amalgamation of error term values.

       ‘I’ denotes that the data values are substituted by the difference in their values with respect to the previous values.

  1. ARCH/GARCH model- autoregressive conditional heteroscedasticity (ARCH) is an extremely unstable model for determining time series forecasting. It is very accurately qualified for detecting dynamic variations of unpredictability from time series data.
  1. Vector autoregressive model or VAR model- it shows the independencies among several time-series data.
  2. LSTM long- It means short term memory (LSTM). It is a steep learning model. It is an RNN (recurrent neural network) that helps to read the dependencies of the sequence.

Setup for time series analysis for machine learning

First of all, important libraries will be imported:

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Then, data will be read from the pandas data frame:

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And here the output:

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In this example, we will be creating a panda’s series named ‘sales’ with a daily frequency data time index. This will only use the daily amount of sales.

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Here, the time-series varies from 01-01-2013 to 31-10-2015. It contains 1034 observations, a standard deviation of 1585 and a mean of 3528. The graph looks like this:

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Trend analysis– It is a component of time-series. It represents distinctions of low, medium and high frequency instabilities. The aim of this study is to see if there is any pattern or any drift in the data. The best tool recommended for this task is visualization.

Let’s create a function that can assist us to determine the trend. We will be seeing some of these statistics in the plot-

  1. Moving average: the mean of previous n data. Also known as “rolling mean”.
  2. Bollinger bands: This is an upper band deviating from the average that is moving and a lower band deviating beneath the moving average.

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When the dataset will complete an observation for the full year, we will begin with 30 days rolling window.:

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A pattern is shown by the plot’s red line. The time-series follows a rectilinear downtrend with hefty points every January. And the trend is noticeable when a rolling widow of one year is used.

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Here we can see that it is a clear rectilinear downtrend. This can be used in model design as it tells us about the trend, whether it is non-linear or linear.

Apart from all the above model, there are other algorithms also that we can include making it more specific like-

  1. Seasonality analysis it is the part of variations in a time-series that represent intra-year fluctuations that are very unstable after years with respect to time, direction, and magnitude.
  2. Stationary test- it is a stochastic process whose unconditional joint probability distribution does not change with time. Other parameters like mean and variance do not change with time which makes stationary time series easier to forecast.
  3. Outlier’s detection: The outlier refers to a data value lying at the footer of the statistical distribution of datasets.

Why is time-series forecasting important in machine learning?

  • So that we can predict the future
  • It helps to succeed in your business

Limitations of time-series:

  • Time-series suffers from a number of limitations. This includes problems with generalization from a single study, difficulty in obtaining the most appropriate measures, and other problems with accuracy.
  • Most of the machine learning algorithms do not deal with time-series properly.

Applications of time-series:

  • Economic forecasting
  • Sales forecasting
  • Yield projections
  • Process and quality control
  • Workload projections
  • Inventory studies
  • Stock market analysis

Conclusion-

We have seen the time series analysis and how it can be used in machine learning models to make it more accurate and specific.

There are various applications of time-series and it is very useful in today’s world to determine the trend and patterns in a particular product.

This article has shown how to analyze real-world time series with statistics and machine learning. The result can be used to create models that are able to fit will with time-series.

In particular:

  • We can include linear trend component in our model
  • We can also train our model on the raw data, which includes outliers on the processed data.
  • We learned that the time series is not stationary. Hence we used the AR-I-MA model instead of using an ARMA.
  • We can also include some seasonal components into our forecasting models.

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