1620377510

Do you know that Asia Pacific is the largest growing deep learning market globally with a CAGR (Compound Annual Growth Rate) of over 40%? Time series TensorFlow prediction is an important concept in deep learning & ML. All the deep learning/ML models have a respective dataset that is a collection of observations. These observations often include a time component. Time series arranges the observations sequentially in time, thus adding a new dimension to the dataset, i.e., time.

One may think about what will be achieved by increasing the dimensionality of their dataset? Well, adding a time dimension to your dataset will create a detailed level of dependence among observations. The outcome is then predicted with the help of time series forecasting of prior observations.

The use of time-series data (historical data) for predicting the future is called time series forecasting. One should also ensure that the future should be completely unknown and can only be predicted based on historical data.

The time-series data is analysed to develop models that describe the data effectively. The time series is decomposed into several components for developing apt models. Once the models that best describe the historical data are developed, it is then used for forecasting. One should not confuse time series analysis with time series forecasting as the latter comes into action only when the former is completed.

#artificial intelligence #time series tensorflow

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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

1620249000

Do you know that Asia Pacific is the largest growing deep learning market globally with a CAGR (Compound Annual Growth Rate) of over 40%? Time series TensorFlow prediction is an important concept in deep learning & ML. All the deep learning/ML models have a respective dataset that is a collection of observations. These observations often include a time component. Time series arranges the observations sequentially in time, thus adding a new dimension to the dataset, i.e., time.

One may think about what will be achieved by increasing the dimensionality of their dataset? Well, adding a time dimension to your dataset will create a detailed level of dependence among observations. The outcome is then predicted with the help of time series forecasting of prior observations.

The use of time-series data (historical data) for predicting the future is called time series forecasting. One should also ensure that the future should be completely unknown and can only be predicted based on historical data.

The time-series data is analysed to develop models that describe the data effectively. The time series is decomposed into several components for developing apt models. Once the models that best describe the historical data are developed, it is then used for forecasting. One should not confuse time series analysis with time series forecasting as the latter comes into action only when the former is completed.

Before knowing about time series TensorFlow forecasting, one should be familiar with the component of a time series. A time series is decomposed into four components during time series analysis. These components help in understanding the dataset properly. The four components of a time series are as follows:

- Trends – The behaviour of a series/dataset over time is explained by trends. The increasing and decreasing behaviour of a time series is explained by trends.
- Level – Level is the base value of the time series, considering that the representation of the series is a straight line. Many experts also define level as the average value of the series.
- Seasonality – The behaviours of the series that are repeated over time are called seasonality. Some experts also term seasonality as periodic fluctuations.
- Noise – Each dataset contains some data points/outliers that the time series model cannot explain. These datasets possess unpredictable properties and cannot be mapped via time series analysis/forecasting.

One should note that a time series must have a level and some noise. However, trends and seasonality in a time series are optional.

#artificial intelligence #time series tensorflow

1620377510

Do you know that Asia Pacific is the largest growing deep learning market globally with a CAGR (Compound Annual Growth Rate) of over 40%? Time series TensorFlow prediction is an important concept in deep learning & ML. All the deep learning/ML models have a respective dataset that is a collection of observations. These observations often include a time component. Time series arranges the observations sequentially in time, thus adding a new dimension to the dataset, i.e., time.

One may think about what will be achieved by increasing the dimensionality of their dataset? Well, adding a time dimension to your dataset will create a detailed level of dependence among observations. The outcome is then predicted with the help of time series forecasting of prior observations.

The use of time-series data (historical data) for predicting the future is called time series forecasting. One should also ensure that the future should be completely unknown and can only be predicted based on historical data.

The time-series data is analysed to develop models that describe the data effectively. The time series is decomposed into several components for developing apt models. Once the models that best describe the historical data are developed, it is then used for forecasting. One should not confuse time series analysis with time series forecasting as the latter comes into action only when the former is completed.

#artificial intelligence #time series tensorflow

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_ is a sequence of time-based data points collected at specific intervals of a given phenomenon that undergoes changes over time. In other words, time series is a sequence taken at consecutive equally spaced points in the time period._Time series

As a example, we can present few time series data sets in different domains such as pollution levels, Birth rates, heart rate monitoring, global temperatures and Consumer Price Index etc. At the processing level, above datasets are tracked, monitored, down sampled, and aggregated over **time.**

There are different kind of time series analysis techniques in the big data analytical field. Among them few are,

- Autoregression (AR)
- Moving Average (MA)
- Autoregressive Moving Average (ARMA)
- Autoregressive Integrated Moving Average (ARIMA)
- Seasonal Autoregressive Integrated Moving-Average (SARIMA)

ARIMA Model

ARIMA Model is simple and flexible enough to capture relationship we would see in the data and It aims to explain the autocorrelation between the data points using past data. We can decompose the ARIMA model as follow to grab the key elements of it.

- **AR: _Auto regression. _**This is a model that uses the dependent relationship between the data and the lagged data.
- **I:_ Integrated. _**The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.
- **MA: _Moving average. _**A model that uses the relationship between the observations and the residual error from the moving average model applied to lagged observations.

Dataset Explanation

Exploratory Analysis

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#python #time-series-analysis #pandas #forecasting #arima #time series analysis using arima model with python

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TensorFlow Probability uses structural time series models to conduct time series forecasting. In particular, this library allows for a “scenario analysis” form of modelling — whereby various forecasts regarding the future are made.

Structural time series modelling takes the inherent characteristics of the time series into account when making forecasts. This includes factors such as the **local linear trend**, **seasonal**, **residual** and **autoregressive** components. The greater the variation surrounding these components — the more uncertain the forecast.

The examples illustrated in this article use the template from the Structural Time Series modeling in TensorFlow Probability tutorial, of which the original authors (Copyright 2019 The TensorFlow Authors) have made available under the Apache 2.0 license.

For this example, a structural time series model is built in TensorFlow Probability to forecast air passenger data. The data is sourced from San Francisco Open Data: Air Traffic Passenger Statistics.

In particular, passenger numbers for United Airlines from February 2014 — June 2020 are analysed. The specific segment of passengers analysed are enplaned, domestic, departing from Terminal 3 at Boarding Area E.

Here is a visual overview of the time series:

#tensorflow #time-series-forecasting #data-science #time-series-analysis #machine-learning #data analysis