Time Series Data and Embeddings¶

In [ ]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px

import matplotlib.pyplot as plt import numpy as np import pandas as pd import plotly.express as px

In [ ]:

def plot_arrow_chart(
    dataframe: pd.DataFrame,
    x_col: str,
    y_col: str,
    ax: plt.Axes,
    color: str = "k",
    alpha: float = 0.7,
    marker: str = ".",
    linestyle: str = "-",
    arrow_head_width: int = 4000,
) -> plt.Axes:
    """
    Plot an arrow chart for the 'Total' and its lagged values
    within a specified date range.
    """

    x = dataframe[x_col].values
    y = dataframe[y_col].values

    ax.plot(x, y, marker=marker, linestyle=linestyle, color=color, alpha=alpha)

    step = max(1, len(x) // 100)
    for i in range(0, len(x) - 1, step):
        ax.arrow(
            x[i],
            y[i],
            x[i + 1] - x[i],
            y[i + 1] - y[i],
            shape="full",
            lw=0,
            length_includes_head=True,
            head_width=arrow_head_width,
            color=color,
            alpha=alpha,
        )

    return ax

def plot_arrow_chart( dataframe: pd.DataFrame, x_col: str, y_col: str, ax: plt.Axes, color: str = "k", alpha: float = 0.7, marker: str = ".", linestyle: str = "-", arrow_head_width: int = 4000, ) -> plt.Axes: """ Plot an arrow chart for the 'Total' and its lagged values within a specified date range. """ x = dataframe[x_col].values y = dataframe[y_col].values ax.plot(x, y, marker=marker, linestyle=linestyle, color=color, alpha=alpha) step = max(1, len(x) // 100) for i in range(0, len(x) - 1, step): ax.arrow( x[i], y[i], x[i + 1] - x[i], y[i + 1] - y[i], shape="full", lw=0, length_includes_head=True, head_width=arrow_head_width, color=color, alpha=alpha, ) return ax

Pendulum¶

In [ ]:

from ts_dl_utils.datasets.pendulum import Pendulum

from ts_dl_utils.datasets.pendulum import Pendulum

In [ ]:

pen = Pendulum(length=20)

df_pen = pd.DataFrame(pen(3, 100, initial_angle=1, beta=0.01))

df_pen["theta_1"] = df_pen["theta"].shift()
df_pen["theta_diff"] = df_pen["theta"].diff()

df_pen

pen = Pendulum(length=20) df_pen = pd.DataFrame(pen(3, 100, initial_angle=1, beta=0.01)) df_pen["theta_1"] = df_pen["theta"].shift() df_pen["theta_diff"] = df_pen["theta"].diff() df_pen

In [ ]:

fig = plt.figure(figsize=(10, 8), layout="constrained")
spec = fig.add_gridspec(2, 2)

ax0 = fig.add_subplot(spec[0, :])
ax10 = fig.add_subplot(spec[1, 0])
ax11 = fig.add_subplot(spec[1, 1])

ax0.plot(
    df_pen.t,
    df_pen.theta,
    marker=".",
    linestyle="-",
    color="k",
)
# Make x-ticks readable
ax0.xaxis.set_major_locator(plt.MaxNLocator(8))
# fig.autofmt_xdate(rotation=30)
ax0.set_title("Swing Angle")


ax10 = plot_arrow_chart(
    df_pen, x_col="theta", y_col="theta_1", ax=ax10, arrow_head_width=0.00001
)
ax10.set_xlabel("Swing Angle")
ax10.set_ylabel("Swing Angle 0.05 seconds ago")
ax10.set_title("Swing Angle and Angle 0.05 seconds ago")


ax11 = plot_arrow_chart(
    df_pen, x_col="theta", y_col="theta_diff", ax=ax11, arrow_head_width=0.00001
)

ax11.set_xlabel("Swing Angle")
ax11.set_ylabel("Swing Angle Change Rate")
ax11.set_title("Phase Portrait")

plt.tight_layout()

fig = plt.figure(figsize=(10, 8), layout="constrained") spec = fig.add_gridspec(2, 2) ax0 = fig.add_subplot(spec[0, :]) ax10 = fig.add_subplot(spec[1, 0]) ax11 = fig.add_subplot(spec[1, 1]) ax0.plot( df_pen.t, df_pen.theta, marker=".", linestyle="-", color="k", ) # Make x-ticks readable ax0.xaxis.set_major_locator(plt.MaxNLocator(8)) # fig.autofmt_xdate(rotation=30) ax0.set_title("Swing Angle") ax10 = plot_arrow_chart( df_pen, x_col="theta", y_col="theta_1", ax=ax10, arrow_head_width=0.00001 ) ax10.set_xlabel("Swing Angle") ax10.set_ylabel("Swing Angle 0.05 seconds ago") ax10.set_title("Swing Angle and Angle 0.05 seconds ago") ax11 = plot_arrow_chart( df_pen, x_col="theta", y_col="theta_diff", ax=ax11, arrow_head_width=0.00001 ) ax11.set_xlabel("Swing Angle") ax11.set_ylabel("Swing Angle Change Rate") ax11.set_title("Phase Portrait") plt.tight_layout()

Covid¶

In [ ]:

df_ecdc_covid = pd.read_csv(
    "https://gist.githubusercontent.com/emptymalei/"
    "90869e811b4aa118a7d28a5944587a64/raw"
    "/1534670c8a3859ab3a6ae8e9ead6795248a3e664"
    "/ecdc%2520covid%252019%2520data"
)

df_ecdc_covid = pd.read_csv( "https://gist.githubusercontent.com/emptymalei/" "90869e811b4aa118a7d28a5944587a64/raw" "/1534670c8a3859ab3a6ae8e9ead6795248a3e664" "/ecdc%2520covid%252019%2520data" )

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px.line(df_ecdc_covid, x="datetime", y="Total")

px.line(df_ecdc_covid, x="datetime", y="Total")

In [ ]:

df_ecdc_covid

df_ecdc_covid

In [ ]:

df_ecdc_covid_arrow_chart = df_ecdc_covid.loc[
    pd.to_datetime(df_ecdc_covid.datetime).between("2020-08-01", "2020-12-01")
].copy()

df_ecdc_covid_arrow_chart["Total_1"] = df_ecdc_covid_arrow_chart["Total"].shift()
df_ecdc_covid_arrow_chart["Total_diff"] = df_ecdc_covid_arrow_chart["Total"].diff()

df_ecdc_covid_arrow_chart = df_ecdc_covid.loc[ pd.to_datetime(df_ecdc_covid.datetime).between("2020-08-01", "2020-12-01") ].copy() df_ecdc_covid_arrow_chart["Total_1"] = df_ecdc_covid_arrow_chart["Total"].shift() df_ecdc_covid_arrow_chart["Total_diff"] = df_ecdc_covid_arrow_chart["Total"].diff()

In [ ]:

fig = plt.figure(figsize=(10, 8), layout="constrained")
spec = fig.add_gridspec(2, 2)

ax0 = fig.add_subplot(spec[0, :])
ax10 = fig.add_subplot(spec[1, 0])
ax11 = fig.add_subplot(spec[1, 1])

ax0.plot(
    df_ecdc_covid_arrow_chart.datetime,
    df_ecdc_covid_arrow_chart.Total,
    marker=".",
    linestyle="-",
    color="k",
)
# Make x-ticks readable
ax0.xaxis.set_major_locator(plt.MaxNLocator(8))
# fig.autofmt_xdate(rotation=30)
ax0.set_title("Covid Cases in EU Over Time")


ax10 = plot_arrow_chart(
    df_ecdc_covid_arrow_chart, x_col="Total", y_col="Total_1", ax=ax10
)
ax10.set_xlabel("Total Cases")
ax10.set_ylabel("Total Cases Lagged by 1 Day")
ax10.set_title("Covid Cases and Lagged Values")


ax11 = plot_arrow_chart(
    df_ecdc_covid_arrow_chart, x_col="Total", y_col="Total_diff", ax=ax11
)

ax11.set_xlabel("Total Cases")
ax11.set_ylabel("Total Cases Change")
ax11.set_title("Covid Cases in EU Phase Portrait")
ax11.set_ylim(-100_000, 100_000)

plt.tight_layout()

fig = plt.figure(figsize=(10, 8), layout="constrained") spec = fig.add_gridspec(2, 2) ax0 = fig.add_subplot(spec[0, :]) ax10 = fig.add_subplot(spec[1, 0]) ax11 = fig.add_subplot(spec[1, 1]) ax0.plot( df_ecdc_covid_arrow_chart.datetime, df_ecdc_covid_arrow_chart.Total, marker=".", linestyle="-", color="k", ) # Make x-ticks readable ax0.xaxis.set_major_locator(plt.MaxNLocator(8)) # fig.autofmt_xdate(rotation=30) ax0.set_title("Covid Cases in EU Over Time") ax10 = plot_arrow_chart( df_ecdc_covid_arrow_chart, x_col="Total", y_col="Total_1", ax=ax10 ) ax10.set_xlabel("Total Cases") ax10.set_ylabel("Total Cases Lagged by 1 Day") ax10.set_title("Covid Cases and Lagged Values") ax11 = plot_arrow_chart( df_ecdc_covid_arrow_chart, x_col="Total", y_col="Total_diff", ax=ax11 ) ax11.set_xlabel("Total Cases") ax11.set_ylabel("Total Cases Change") ax11.set_title("Covid Cases in EU Phase Portrait") ax11.set_ylim(-100_000, 100_000) plt.tight_layout()

Walmart¶

In [ ]:

df_walmart = pd.read_csv(
    "https://raw.githubusercontent.com/datumorphism/"
    "dataset-m5-simplified/refs/heads/main/dataset/"
    "m5_store_sales.csv"
)

df_walmart = pd.read_csv( "https://raw.githubusercontent.com/datumorphism/" "dataset-m5-simplified/refs/heads/main/dataset/" "m5_store_sales.csv" )

In [ ]:

df_walmart

df_walmart

In [ ]:

px.line(df_walmart, x="date", y="CA")

px.line(df_walmart, x="date", y="CA")

In [ ]:

df_walmart_total = df_walmart[["date", "CA", "TX", "WI"]].copy()

df_walmart_total = df_walmart[["date", "CA", "TX", "WI"]].copy()

In [ ]:

df_walmart_total["total"] = (
    df_walmart_total.CA + df_walmart_total.TX + df_walmart_total.WI
)

df_walmart_total["datetime"] = pd.to_datetime(df_walmart_total.date, format="%Y-%m-%d")
df_walmart_total["timestamp"] = df_walmart_total.datetime.astype(int) // 10**9

df_walmart_total["total"] = ( df_walmart_total.CA + df_walmart_total.TX + df_walmart_total.WI ) df_walmart_total["datetime"] = pd.to_datetime(df_walmart_total.date, format="%Y-%m-%d") df_walmart_total["timestamp"] = df_walmart_total.datetime.astype(int) // 10**9

In [ ]:

df_walmart_total["total_1"] = df_walmart_total.total.shift()
df_walmart_total["total_diff"] = df_walmart_total.total.diff()

df_walmart_total["total_1"] = df_walmart_total.total.shift() df_walmart_total["total_diff"] = df_walmart_total.total.diff()

In [ ]:

px.scatter(
    df_walmart_total.loc[pd.to_datetime(df_walmart_total.date).dt.year == 2016],
    x="total",
    y="total_1",
    color="timestamp",
)

px.scatter( df_walmart_total.loc[pd.to_datetime(df_walmart_total.date).dt.year == 2016], x="total", y="total_1", color="timestamp", )

In [ ]:

df_walmart_arrow_chart = df_walmart_total.loc[
    pd.to_datetime(df_walmart_total.date).between("2016-01-01", "2016-03-01")
].copy()

df_walmart_arrow_chart = df_walmart_total.loc[ pd.to_datetime(df_walmart_total.date).between("2016-01-01", "2016-03-01") ].copy()

In [ ]:

fig = plt.figure(figsize=(10, 8), layout="constrained")
spec = fig.add_gridspec(2, 2)

ax0 = fig.add_subplot(spec[0, :])
ax10 = fig.add_subplot(spec[1, 0])
ax11 = fig.add_subplot(spec[1, 1])

ax0.plot(
    df_walmart_arrow_chart.datetime,
    df_walmart_arrow_chart.total,
    marker=".",
    linestyle="-",
    color="k",
)
# Make x-ticks readable
ax0.xaxis.set_major_locator(plt.MaxNLocator(8))
# fig.autofmt_xdate(rotation=30)
ax0.set_title("Walmart Sales Over Time")

ax10 = plot_arrow_chart(
    df_walmart_arrow_chart,
    x_col="total",
    y_col="total_1",
    ax=ax10,
    arrow_head_width=500,
)
ax10.set_xlabel("Total Sales")
ax10.set_ylabel("Total Sales Lagged by 1 Day")
ax10.set_title("Walmart Sales and Lagged Sales")

ax11 = plot_arrow_chart(
    df_walmart_arrow_chart,
    x_col="total",
    y_col="total_diff",
    ax=ax11,
    arrow_head_width=500,
)
ax11.set_xlabel("Total Sales")
ax11.set_ylabel("Total Sales Change")
ax11.set_title("Walmart Sales Phase Portrait")


plt.tight_layout()

fig = plt.figure(figsize=(10, 8), layout="constrained") spec = fig.add_gridspec(2, 2) ax0 = fig.add_subplot(spec[0, :]) ax10 = fig.add_subplot(spec[1, 0]) ax11 = fig.add_subplot(spec[1, 1]) ax0.plot( df_walmart_arrow_chart.datetime, df_walmart_arrow_chart.total, marker=".", linestyle="-", color="k", ) # Make x-ticks readable ax0.xaxis.set_major_locator(plt.MaxNLocator(8)) # fig.autofmt_xdate(rotation=30) ax0.set_title("Walmart Sales Over Time") ax10 = plot_arrow_chart( df_walmart_arrow_chart, x_col="total", y_col="total_1", ax=ax10, arrow_head_width=500, ) ax10.set_xlabel("Total Sales") ax10.set_ylabel("Total Sales Lagged by 1 Day") ax10.set_title("Walmart Sales and Lagged Sales") ax11 = plot_arrow_chart( df_walmart_arrow_chart, x_col="total", y_col="total_diff", ax=ax11, arrow_head_width=500, ) ax11.set_xlabel("Total Sales") ax11.set_ylabel("Total Sales Change") ax11.set_title("Walmart Sales Phase Portrait") plt.tight_layout()

Electricity Data¶

In [ ]:

import io
import zipfile

import io import zipfile

In [ ]:

import pandas as pd

import pandas as pd

In [ ]:

import requests

# Download from remote URL
data_uri = "https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip"

r = requests.get(data_uri)

z = zipfile.ZipFile(io.BytesIO(r.content))
z.extractall("tmp/data/uci_electricity/")

import requests # Download from remote URL data_uri = "https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip" r = requests.get(data_uri) z = zipfile.ZipFile(io.BytesIO(r.content)) z.extractall("tmp/data/uci_electricity/")

In [ ]:

# Load as pandas dataframe
df_electricity = (
    pd.read_csv("tmp/data/uci_electricity/LD2011_2014.txt", delimiter=";", decimal=",")
    .rename(columns={"Unnamed: 0": "date"})
    .set_index("date")
)
df_electricity.index = pd.to_datetime(df_electricity.index)

# Load as pandas dataframe df_electricity = ( pd.read_csv("tmp/data/uci_electricity/LD2011_2014.txt", delimiter=";", decimal=",") .rename(columns={"Unnamed: 0": "date"}) .set_index("date") ) df_electricity.index = pd.to_datetime(df_electricity.index)

In [ ]:

df_electricity

df_electricity

In [ ]:

df_electricity.loc[
    (df_electricity.index >= "2012-01-01") & (df_electricity.index < "2012-02-01")
][["MT_001"]].plot()

df_electricity.loc[ (df_electricity.index >= "2012-01-01") & (df_electricity.index < "2012-02-01") ][["MT_001"]].plot()

---

Contributors: LM