Learning any machine learning technique or any programming language, in general, is not easy. Passion should be the driving force if you want to master coding like a pro. Learning programming takes a lot of time, effort, and energy. In a way, learning machine learning and programming progress is a type of time-series data, where your experience grows over time.

This is why a lot of college students when dealing with their writing assignments prefer to buy college essays so they can free up more time to study coding. The reason it takes you so much time is that you have not only to learn the language of programming but also have to explore different libraries like these we are going to take a look at in this article.

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Time series simply represent data points over time. Thus, they are everywhere in nature and business: temperature, heartbeat, birth rate, population dynamics, Internet traffic, inventories, stocks, sales, orders, factory production – anything. In countless cases, effective processing and prediction of time series can provide decisive benefits.

It can help businesses adapt their strategies in advance (e.g., if production can be scheduled in advance) or improve their operations (e.g., by detecting anomalies in complex systems). Although there are many models and tools for time series, it is still often nontrivial to work with them because each has its complexities and cannot always be used in the same way.

In this article we cover:

**Sktime****Flint****Darts****Pyflux****Prophet**

**IMPORTANT NOTE:** Before using any of these libraries make sure that you install Python 3.6 or higher and C++ 14 or higher.

## 1. Sktime

Sktime is an open-source Python-based machine learning toolset designed specifically for time series. This project is being sponsored and run by the community of the British Economic and Social Research Council, the Consumer Data Research Center, and the Alan Turing Institute.

Sktime provides an extension to the scikit-learn API for time-series solutions. It contains all the essential algorithms and tools for the effective resolution of time-series regression, prediction, and categorization issues. The library includes special machine learning algorithms and conversion methods for time series that other popular libraries do not have.

Sktime was developed to work with scikit-learn, effortlessly adjusting algorithms for connected time series problems and constructing complex models. How does it work? Many time series problems are somehow connected to each other. An algorithm that can be applied to one problem can very often be used to solve another associated problem as well. This concept is known as reduction.

For instance, a model for time-series regression (which utilizes a series to forecast the outcome value) can be rerun for a time-series prediction problem (which predicts the outcome value – the value that will be obtained in the future).

### 1.1 Sktime Installation

To install sktime via pip, use following command:

` pip install sktime`

### 1.2 Sktime Code Example

Here is an example of how sktime can be used:

```
from sktime import datasets
from sktime.forecasting import model_selection
from sktime.utils.plotting import plot_series
y = datasets.load_airline()
y_train, y_test = model_selection.temporal_train_test_split(y)
plot_series(y_train, y_test, labels=["y_train", "y_test"])
```

## 2. Flint

The ability to analyze time-series data on a huge scale is now very much in demand both in production automation systems and financial applications, as well as in the Internet of Things (IoT, Internet of Things) platforms. The de facto standard for big data processing today is the Apache Spark computational framework.

It contains many built-in and pluggable libraries, one of which is Two Sigma’s product, the open-source Flint library for fast parallel time-series operations. Flint takes advantage of the natural ordering of time series data to provide location-based optimization.

The Flint library is available via Maven and PyPI. The entry point for all time series analysis functions in Flint is TimeSeriesRDD for the Scala API and TimeSeriesDataFrame for the Python API. At a high level, TimeSeriesRDDD contains OrderedRDD, which can be used to represent a sequence of ordered key-value pairs. TimeSeriesRDDD uses Long to represent timestamps in nanoseconds from the beginning of an epoch as keys and InternalRows as values for OrderedRDD to represent a time series dataset.

Unlike DataFrame and Dataset, TimeSeriesRDD Flint can take advantage of the existing ordering properties of data sets at rest and the fact that almost all data operations and analysis of these data sets take their temporal ordering properties into account. It differs from other time series in Spark in its ability to efficiently perform calculations on panel data or large-scale, high-frequency data.

### 1.1 Flint Installation

To install sktime via pip, use following command:

` pip install ts-flint`

### 1.2 Sktime Code Example

Here is an example of how Flint can be used:

```
from ts.flint import FlintContext, summarizers
flintContext = FlintContext(sqlContext)
df = spark.CreateDataFrame(
[("2021-08-20", "1.0"), ("2021-08-21", "2.0"), ("2021-08-23", "3.0")],
["time", "v"]
).withColumn("time", from_uts_timestamp(col("time"), "UTC"))
flint_df = flintContext.read.dataframe(df)
flint_df = flint_df.withColumn('v', flint_df['v'] + 1)
flint_df = flint_df.summarizeCycles(summarizers.count())
```

## 3. **Darts**

If you’re a data scientist who works with time series, then you already know this: time series are peculiar brutes. With ordinary data in tables, you can frequently just use scikit-learn to do most machine learning tasks, starting from preprocessing to forecasting and model choosing. But that’s not the case with time series. You can easily find yourself in scenarios where you need one library for preprocessing, a second for seasonality determination, another for matching the prediction model, and, finally, oftentimes you will have to develop your own testing and model selection procedures.

This can become quite exhausting since the majority of libraries use various APIs and data types. This is not to mention cases related to more complicated models that are based on neural grids, or issues involving outside data and other dimensions. You will probably have to develop your models for your particular application, for example via libraries such as Tensorflow or PyTorch. All in all, we believe that the time-series machine learning experience in Python is still not completely seamless.

We are big supporters of the scikit-learn method: a unified open-source library with a coherent API that provides an excellent set of tools for end-to-end machine learning. Darts strives hard to understand time-series learning, so its core aim is to make the whole process of machine learning time series easier.

### 3.1 Darts Installation

To install sktime via pip, use following command:

` pip install darts`

### 2.2 Darts Code Example

Here is an example of how darts can be used:

```
import pandas as pd
import matplotlib.pyplot as plt
from darts import TimeSeries
from darts.models import ExponentialSmoothing
df = pd.read_csv("AirPassengers.csv")
series = TimeSeries.from_dataframe(df, "Month", "#Passengers")
train, val = series.split_before(pd.Timestamp("19580101"))
model = ExponentialSmoothing()
prediction = model.predict(len(val))
series.plot(label="actual")
prediction.plot(label="forecast", lw=3)
plt.legend()
```

## 4. PyFlux

Pyflux is an open-source library of time series designed for Python. Pyflux selects a more likelihood-based approach for dealing with time series issues. This approach is particularly useful for problems such as prediction, where a more complete picture of uncertainty is needed.

Users can construct a stochastic pattern in which data and hidden values are processed as random hazards by using joint probability.

Sktime was developed to work with scikit-learn, effortlessly adjusting algorithms for connected time series problems and constructing complex models. How does it work? Many time series problems are somehow connected to each other. An algorithm that can be applied to one problem can very often be used to solve another associated problem as well. This concept is known as reduction.

For instance, a model for time-series regression (which utilizes a series to forecast the outcome value) can be rerun for a time-series prediction problem (which predicts the outcome value – the value that will be obtained in the future).

### 4.1 PyFlux Installation

To install sktime via pip, use following command:

` pip install pyflux`

### 4.2 PyFlux Code Example

Here is an example of how pyflux can be used:

```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pyflux as pf
from pandas_datareaded.data import DataReader
from datetime import datetime
reader = DataReader("JPM", "yahoo", datetime(2006, 6, 1), datetime(2016, 6, 1))
reader_data = pd.DataFrame(np.diff(np.log(reader["Adj Close"].values)))
reader_data.index = reader.index.values[1:reader.index.values.shape[0]]
reader_data.columns = ["JPM Returns"]
model = pf.GARCH(p = 1, q = 1, data = reader_data)
result = model.fit("M-H", nsims = 20000)
model.plot_z([1, 2])
model.plot_fit(figsize=(15,5))
model.plot_predict(h=30, figsize=(15,5))
```

## 5. Prophet

The Prophet is an open-source library dedicated to making predictions for one-dimensional time-series datasets. Being user-friendly, it is intended to automatically search for the right range of the model hypersets to make accurate predictions for data with the trend and seasonal structure by default.

The Prophet realizes an additive time series prediction model, and this realization maintains trends, seasons, and holidays.

The library offers two interfaces, including R and Python.

### 5.1 Prophet Installation

To install sktime via pip, use following command:

` pip install sktime`

### 5.2 Prophet Code Example

Here is an example of how prophet can be used:

```
import pandas as pd
import matplotlib.pyplot as plt
from fbprophet import Prophet
df = pd.read_csv("AirPassengers.csv")
model = Prophet()
model.fit(df)
future_df = model.make_future_dataframe(periods=730)
forecast = model.predict(future_df)
plot = model.plot(forecast)
complot = model.plot_components(forecast)
plt.show()
```

## Conclusion

In this article, we had a chance to check out some of the best Python libraries for working with time-series data. Make sure you provide yourself with enough free time that you’ll spend exploring these libraries. You can also check helpful reddit reviews out to find services that will be useful when the hard times in college come. If you are passionate about Python make sure you spend every minute you can exploring it. And very soon you’ll be rewarded.

Thank you for reading!

This bundle of e-books is specially crafted for **beginners**.

Everything from Python basics to the deployment of Machine Learning algorithms to production in one place.

Become a Machine Learning Superhero **TODAY**!

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